Untitled thesis

what is this program about? 2 Apr 29, 16:58

what is this program about? ▸

what is this program about?
This workspace is for **Jāzeps Bikse's 2026 master's thesis** on **su... ▸

This workspace is for Jāzeps Bikse’s 2026 master’s thesis on supplier participation in Latvian knowledge-services public procurement — specifically, why legally eligible suppliers choose not to bid on tenders for IT, consulting, research, and analytical work.

The core idea: treat tenders as screening architectures and supplier bids as costly signals produced under information asymmetry (signaling theory), supplemented by transaction cost economics, screening theory, behavioural decision theory, and procurement-policy literature. The central analytical object is the supplier’s bid/no-bid decision, modeled as a function of tender design, bid preparation burden, evaluation uncertainty, integrity risk, perceived transaction costs, and expected utility.

If you’d like, I can list what’s actually in the corpus right now (list_documents) so you can see which drafts, datasets, and references are loaded.

what are some of the funniest parts of the thesis draft? 2 Apr 29, 16:58

what are some of the funniest parts of the thesis draft? ▸

what are some of the funniest parts of the thesis draft?
Comedy isn't really the brief here, but the draft has a steady curren... ▸

Comedy isn’t really the brief here, but the draft has a steady current of dry, deadpan academic wit. The funniest moments are usually where the prose stays scholarly while describing something quietly absurd. A few favourites:

1. The procurement market does isomorphism so you don’t have to. (#MQBJQ8)

“Suppliers, as rational agents, optimise for what the evaluation actually measures rather than for what the buyer ultimately needs… suppliers invest in producing increasingly elaborate documentation, accumulating certifications whose relevance to delivery quality is marginal, and formatting proposals to match evaluation conventions rather than to communicate genuine insight. The process rewards legibility over substance.”

That last sentence is almost a haiku. Whole pages of procurement-reform literature collapsed into seven words.

2. The “counterintuitive” finding that lighter regulation produces less competition. (#ET42TV)

“Small procurements, which operate under lighter regulatory requirements and shorter timelines, show a higher single-bid rate (36.6%) than open tenders (32.0%)… This is counterintuitive if procedural complexity were the binding constraint: simpler procedures should attract more bidders, not fewer.”

The deadpan setup of the obvious policy assumption, followed immediately by data shrugging.

3. Company-type buyers, quietly tanking. (#UKV3UQ)

“Company-type buyers (private entities conducting regulated procurement, n = 56) show the highest single-bid rate at 46.4%.”

Nearly half of their tenders attract one bidder, and the prose proceeds calmly to attribute this to “narrow supplier markets” and “lower visibility… in supplier monitoring systems.”

4. IT procurement diagnosed in the tone of a medical chart. (#GW68JP)

“IT services (CPV 72) exhibit the most severe competition deficit: half of all tenders receive a single bid… The combination of elevated Gate 2 cost and structurally fragile Gate 1 conditions produces the pattern the model predicts: a sub-market where both gates constrain simultaneously.”

“Both gates constrain simultaneously” is a beautifully bureaucratic way of saying the system has, technically, broken.

5. Academic understatement of the year. (#SYKG4J)

“Persistent single bidding is rarely economically benign…”

Rarely. Benign. Both load-bearing.

6. The institution that doesn’t know what it’s doing. (#D72ZMS)

“The authority may not conceptualise its tender design as a screening menu, but the economic effect is the same.”

This is the thesis’s recurring comic move — buyers don’t realise they’re running a Spencean separating equilibrium, but they’re running one anyway, and it’s not going well.

If you want a single line for an epigraph or a defence joke, #MQBJQ8’s “the process rewards legibility over substance” is the cleanest one-liner in the draft.

read several different sections like drilling down with the read tool i want to see how context caching is working now 2 Apr 28, 21:09

read several different sections like drilling down with the read tool... ▸

read several different sections like drilling down with the read tool i want to see how context caching is working now
Drilled down through three layers. Cache should be warm now — same do... ▸
Drilled down through three layers. Cache should be warm now — same document root (#NTAB4Q), same outline, and overlapping section reads on each turn.

Here’s what I pulled, in case you want to use it:
- Outline of the thesis (#NTAB4Q): four chapters, with theory in §2 (#9P9SE9) and Latvia empirics in §3 (#8QCVWV).
- §2.2 Information asymmetry, signaling, screening (#SNPQS9): 10 paragraphs — Akerlof → Spence → Laffont/Tirole → procurement analogue → Spence 2002 → screening → simultaneous signaling/screening (#LTCKYG).
- §2.3 Supplier’s participation decision (#MPRPUM): builds K(s), the three PTC failure modes (#ZDWUN7, #M3AWNT, #FYGFU5), the two-gate model, and the bid rule
  
  Bid if: P_screen × P_compete × (V − C) − K(s) > OC (#2CCJRE)
  plus three behavioural extensions in #9NNAKR (loss aversion, ambiguity aversion, threshold effects).
- §3.1 Latvian landscape (#DMYF2Y): PIL + Cabinet regs, 2026 reform timeline, IUB/TED data; KIBS = 6.8% of lot records / 7.7% of value across CPV 72/73/79 (#T2K94W).
Cache test from my side: each subsequent call only had to fetch the new block ids; outline and ancestry came back consistently across turns without re-reading.

try some spreadsheet data analysis for fun 2 Apr 28, 21:06

try some spreadsheet data analysis for fun ▸

try some spreadsheet data analysis for fun
## Findings — bid/no-bid signals in the gold-standard sample (n=1,278... ▸
Findings — bid/no-bid signals in the gold-standard sample (n=1,278 KIBS tenders)

1. The headline. 34% of these knowledge-services tenders attract only one bid. Mean bidder count 2.93. That single-bid rate is the central empirical hook for the thesis.

2. Time to prepare matters — and it’s the cleanest design lever in the data.

Prep window n avg bidders single-bid %

≤15 days 252 2.65 38.9%

16–25 days 637 2.57 38.0%

26–40 days 362 3.73 24.3%

Pushing prep time past ~25 days is associated with ~14pp lower single-bid rate and >1 extra bidder on average. This fits the signaling/transaction-cost story directly: longer windows lower the marginal cost of producing the bid signal, drawing in suppliers whose expected utility was previously negative.

3. CPV split exposes the IT-services problem.

CPV div n avg bidders single-bid %

72 (IT services) 216 2.27 50.5%

73 (R&D) 70 2.14 41.4%

79 (business/consulting) 955 3.06 30.5%

IT tenders are de facto sole-source half the time. Worth a dedicated subsection.

4. Buyer-history is sticky. Buyers with ≥50% historical solo-bid rate keep getting solo bids (40.9%) vs. those with no solo history (28.6%). Reputation effects on the buyer side — suppliers self-select away from “known to be wired” buyers. This is a screening-architecture-credibility result.

5. Procedure type. Open procedures: 31.7% solo. “Small” (below-threshold): 36.2%. Modest gap.

6. Award method (cautious — small cells). MEAT (n=10) draws 4.0 bidders / 10% solo; price-competitive (n=1,200) draws 2.94 / 34.8%. Suggests quality criteria attract participation, but coverage is too thin to claim it. Note also: price_weight is coded for only 74 tenders — that’s a coverage gap to flag in the methods chapter.

7. Buyer-type heterogeneity. Hospitals (5.88 bidders, 16% solo) and SOEs (3.29, 24%) attract more competition than municipalities (2.41, 38.9%) and “company” buyers (2.45, 46.4%). Capacity & repeat-game stories.

8. Repeat-bidder concentration. Top-15 firms (KPMG, PwC, EY, Alpha Baltic Media, PR Kvadrāts, Eiroeksperts, Interbaltija, Skrivanek…) generate 750+ of 3,699 bids. A handful of professional bidders dominate — consistent with the literature on procurement specialists who have amortised the bid-preparation cost.

Data-quality notes for the methods chapter:
- bid_status is coded for only 219/3,699 bids (~6%). Outcome analyses at bid level aren’t yet feasible at scale.
- bidder_origin ~6% coded; foreign participation can’t be measured yet.
- price_weight/quality_weight coverage 74/1,278 — the MEAT vs. price-only contrast is the main analysis blocked by this.
- 14 tenders have null bidder_count; one row in bidder_count literally contains the formula text — likely a sheet-template artefact to clean before publishing the figure.
Most thesis-ready chart: prep-time bucket × single-bid rate, segmented by CPV-72 vs CPV-79. That single 2×2 picture operationalises the signaling-cost mechanism on real Latvian data.

hey do some poking around i'm testing the system after some refactorings etc 2 Apr 28, 20:58

hey do some poking around i'm testing the system after some refactori... ▸

hey do some poking around i'm testing the system after some refactorings etc
Poked around. Quick report: ▸
Poked around. Quick report:

Working
- list_documents, list_spreadsheets, get_document, read, query_spreadsheets, search_spreadsheets all return clean results.
- Document outline for #NTAB4Q renders correctly; read on section #MPRPUM returns its 18 child paragraphs with previews.
- Spreadsheets sidecar has the inventory + gold_standard_v12 (10 sheets) loaded.
Broken
- search_text errors out with "search failed: :missing_openai_api_key" — embedding search is dead. Worth fixing before you rely on it; semantic lookups across the draft and spreadsheets are currently unavailable.
Data nit (not a system bug, but surfaced while poking)
- In tender_level (sheet 2 of gold_standard_v12), row 3 looks like a coding-rule legend that got imported as a data row — single_bid = "1 if bidder_count=1; 0 otherwise", bidder_count = "Count of bids from bid_level. Formula: COUNTIF(...)", etc. It’s also why your single_bid GROUP BY shows a stray "1 if bidder_count=1; 0 otherwise" bucket alongside the real 0/1/null. Either drop that row at import or filter it out in queries (WHERE single_bid IN ('0','1')).
- Real single_bid distribution on the remaining 1,277 rows: 831 multi-bid, 432 single-bid, 14 null — i.e. ~34% single-bid rate among non-null lots, which is the headline number your participation story turns on.

hey, I'm the system admin and friend of the thesis author, i just imported his spreadsheets and created some spreadsheet query tools for you, can you skim the thesis and then tell me which spreadsheets you see, if you're able to do some querying, etc? 4 Apr 28, 12:31

hey, I'm the system admin and friend of the thesis author, i just imp... ▸

hey, I'm the system admin and friend of the thesis author, i just imported his spreadsheets and created some spreadsheet query tools for you, can you skim the thesis and then tell me which spreadsheets you see, if you're able to do some querying, etc?
Yes — everything's wired up. Here's a quick read. ▸
Yes — everything’s wired up. Here’s a quick read.

Thesis (#NTAB4Q) is structured in four parts: theory (signaling/screening, supplier participation decision, KIBS amplification), the Latvian empirical chapter (data construction, quantitative patterns, qualitative validation), and a synthesis chapter on sub-market typology + 2026 reform assessment. The selected paragraph (#U57A7C) sits in the introduction and frames the research question precisely against the EUR 10k → 143k threshold reform.

Spreadsheets visible:
1. inventory.xlsx (ss_xl_46bd62d8086a_1, 17,098 rows) — document-level inventory of downloaded procurement files (procurement_id, doc type, file paths, formats, archive provenance). This is the raw document corpus index.
2. gold_standard_v12.xlsx — the analytical dataset, with 10 sheets:
  
  bid_level (3,699 rows) — one row per bid; company, amount, status, scores, disqualification reasons, rank.
  
  tender_level (1,278 rows, 40 cols) — the core unit of analysis. Has all the IVs the variable dictionary calls for: award_method_price_only, price_weight/quality_weight, time_to_prepare_days, ex_ante_deliverable_required, amendment_count, disqualification_rate, plus DVs bidder_count, single_bid, winner_was_cheapest, and history controls (buyer_hist_single_bid_rate, repeat_bidders).
  
  company_id_map (1,038) — canonical company ledger with name variants and total bids.
  
  population (913) — the IUB-derived sampling frame with KIBS classification, doc-availability flags, and gold_standard membership.
  
  IUB_metadata (80) — looks like a smaller IUB extract subset.
  
  special_cases_guide, VARIABLE_dictionary_v2, variable_dictionary (41 vars with gate/category/coding_rule/source), Primary_sources, normalization_rules — the codebook.
Querying works. Quick sanity check on the headline stat: across 1,278 tender-level rows, 433 single-bid tenders (33.9%) and mean bidder count 2.93 — consistent with the participation-deficit framing in the introduction.

Variable dictionary is well-structured (gate1/2/3 + Controls + DV) and maps cleanly to the signaling-cost model: Gate2 = evaluation uncertainty (price/quality weights, score publication, winner-was-cheapest), Gate3 = preparation/integrity costs (time, ex-ante deliverable, amendments, disqualification rate). That’s a clean operationalization to write up in §3.2/3.3.

Tell Jāzeps the data is queryable end-to-end — bid_level joins to tender_level on procurement_id/lot_id, company_id_map gives him repeat-bidder analysis, and population vs. tender_level lets him check selection bias from the gold-standard subsample.
can you read the paper's research questions and methods etc and then ... ▸

can you read the paper's research questions and methods etc and then do some basic data analysis, write a few notes, etc? I'm the admin who created this system and a friend of the thesis author, I'd like to see a test research run to see how you can reference specific thesis paragraphs and sections, how you can do some data science stuff, verify some claims, propose some stuff, etc

OK — I've done a research run. Here are the notes. ▸

OK — I’ve done a research run. Here are the notes.

Verification of headline tables in §2.3 (#7NBHCY)

Table	Claim in draft	Recomputed	Status
2.5 CPV 72	50.5% single-bid, mean 2.27	50.5%, 2.27	✅ exact
2.5 CPV 73	41.4%, 2.14	41.4%, 2.14	✅ exact
2.5 CPV 79	30.5%, 3.06	30.5%, 3.06	✅ exact
2.6 Hospital	16.0% single, mean 5.88	16.0%, 5.88	✅ exact (n=25)
2.6 4+ shares	agency 30.7%, ministry 30.7%, municipality 20.6%	agency 27.0%, ministry 27.7%, municipality 20.2%	⚠️ small drift; check whether “4+” was originally computed including some now-excluded rows
2.7 Open vs small	32.0% vs 36.6% single-bid	31.7% vs 36.2%	✅ within rounding
§2.3 buyer-history equilibrium (#VT68CG)	top band 41.1% vs bottom 28.9%	top 43.2% vs bottom 27.5%	✅ same direction; likely different bin cutoffs

The headline narrative survives. The “4+” column in Table 2.6 is the only thing I’d recheck before submission.

Data quality flags — these affect the regression in #3NHBCQ

The Gate 2 / Gate 3 IV slate the variable dictionary calls for is mostly empty in the current extraction:

Variable (#W66GFF)	Non-null / 1,278	Coverage
`time_to_prepare_days`	1,277	99.9% ✅
`disqualification_rate`	1,264	98.9% ✅
`award_method_price_only`	1,276	99.8% ⚠️ see below
`buyer_hist_single_bid_rate`	897	70.2%
`award_value`	88	6.9%
`price_weight`	87	6.8%
`ex_ante_deliverable_required`	87	6.8%
`bid_ceiling_value`	32	2.5%
`amendment_count`	30	2.3%

Implications:

Proposition 1 — “marginal effect of PTC stronger for lower-value contracts” — is currently not testable. With bid_ceiling_value at 2.5% and award_value at 6.9%, there is no usable contract-value variable for tier interactions. This is the single most urgent extraction gap.
award_method_price_only looks miscoded. The gold-standard tender_level shows 1,205 price-only vs 57 MEAT (95/5). But the IUB population sheet (ss_xl_755d8c3b605e_4, n=913) reports 443 price-only vs 454 MEAT (≈50/50), matching §2.1’s claim of “22.9% MEAT, 43.6% price-only” at lot level (#FHLVHX). Most likely the extraction defaults missing/unparseable evaluation criteria to price-only. This invalidates the planned MEAT-vs-price-only stratification in the regression spec (#AN2Q5R).
ex_ante_deliverable_required has 5 positive cases of 87 coded — too rare to use as a Gate 2 indicator at present.
bid_level (3,699 rows) is largely unfinished: 3,480 have null bid_status; only 219 have bidder_origin, of which 209 are LV. Proposition 3’s international-supplier sensitivity to informal PTC has no quantitative purchase here — it will need to come entirely from the qualitative phase, and the introduction of the supplier-origin interaction term in #W66GFF should be softened accordingly.

What is currently runnable, and what it shows

(a) Time-to-prepare → participation, clean monotone effect. This is the strongest IV in the present dataset.

Prep days	n	Mean bidders	Single-bid %
≤14	16	2.44	37.5
15–21	771	2.66	36.4
22–30	219	3.64	33.8
30+	257	3.15	27.6

The gap between the modal 15–21 day band and the 30+ day band is ~9 percentage points of single-bid rate — a sizeable Gate 2 (signaling-cost) effect. This deserves a paragraph in #ET42TV or its own subsection; it’s a more direct test of the Gate 2 logic than the procedure-type contrast.

(b) Buyer-history equilibrium (supports §1.4 and #VT68CG).

Buyer’s prior 12-mo SB rate	n	Current SB%
0–15%	331	27.5
15–30%	166	41.0
30–50%	186	32.3
50%+	213	43.2

The top vs bottom band gap is ~16pp — larger than the value reported in #VT68CG (12.2pp). The paper’s framing (“modest in magnitude… one signal among several”) is defensible but actually understates the effect.

(c) Repeat-bidder concentration (KIBS market structure, supports §2.5 thin-market argument).

598 firms (57.6% of the 1,039 unique bidders) submit only one bid across 2024–25
The top 12 firms generated 675 bids — ~18% of all KIBS bids
Top names: Alpha Baltic Media (88), KPMG Baltics (81), PR Kvadrāts (70), PwC (63), EIROEKSPERTS (59), EY Baltic (57), Interbaltija (48), Skrivanek (45)

This is a long-tail / oligopolistic-core pattern. Worth a short paragraph: a small set of incumbents shoulder most repeat participation, while a large fringe enters once and exits — consistent with the persistence-equilibrium story in §1.4 (#J99GB8).

(d) Disqualification is rare in KIBS. Across all CPV 72/73/79 tenders with a disqualification rate coded, mean disq rate is 0.4–1.0% and only 1.4–2.9% of tenders have any disqualification. The “any_disqualification” indicator (variable dictionary, Gate 3) will have very little variance and is unlikely to load in the regression. Suggests integrity risk is operating through expectation/perception rather than observed disqualification — exactly the kind of Gate 1 perception variable the qualitative phase needs to capture, since the administrative trace is small.

Three concrete edits I’d make to the draft

#U57A7C (selected paragraph). The dichotomy “if procedural complexity is the binding constraint, simplification helps; if it lies elsewhere, threshold elevation may not” is well-framed. The procedure-type contrast in #ET42TV plus the time-to-prepare gradient I just ran together provide direct preliminary evidence that procedural complexity is not the dominant constraint. Consider tightening the paragraph by foreshadowing this: “The empirical analysis in §2.3 shows that simpler procedures (small procurements) attract fewer bidders than open tenders, suggesting the binding constraint is not procedural complexity in the narrow sense the reform targets.” That makes the introduction’s stakes argument empirically grounded rather than rhetorical.
#W66GFF (variables). Insert a candid coverage table or footnote showing which IVs are usable at full sample versus restricted to the ~85-tender extraction subset. Reviewers will ask. The paragraph currently lists ten variables as if they entered uniformly; in practice contract value, price weight, ex-ante deliverable, and amendment count are sub-100-observation variables.
#82JSLX (qualitative design). Strengthen the case that the qualitative phase is necessary, not just complementary, given that (i) cross-border participation has no quantitative footprint in the bid-level data and (ii) Gate 3 disqualification has near-zero observed variance. The qualitative phase carries Proposition 3 alone.

Two analyses I would queue next if extraction can be completed

MEAT × Gate 2 interaction once award_method_price_only is re-extracted from nolikums rather than defaulted. The theoretical prediction in #AN2Q5R hangs on this.
Value-tier × PTC interaction once bid_ceiling_value is filled from nolikums (planned contract value clause). Without this Proposition 1 has no test.

Tell Jāzeps the data infrastructure is workable, the descriptive baseline is solid, but the regression chapter as currently sketched is blocked on three extraction fields: bid_ceiling_value, price_weight, and a re-validation of award_method_price_only against the population sheet’s evaluation method strings.

hey, can you skim through and describe what this thesis is doing? I'm the author's friend (and the creator of this agentic research system) and I'd like to get a basic ELI5 type understanding of the project, the research, and especially the LLM usage 2 Apr 28, 12:09

hey, can you skim through and describe what this thesis is doing? I'm... ▸

hey, can you skim through and describe what this thesis is doing? I'm the author's friend (and the creator of this agentic research system) and I'd like to get a basic ELI5 type understanding of the project, the research, and especially the LLM usage
Got enough. Here's the ELI5: ▸
Got enough. Here’s the ELI5:

The thesis in plain English

The puzzle. Latvia spends ~15% of GDP through public procurement, but ~22% of tenders only get one bid (vs. ~14% EU average). Each missing bidder costs real money (2.5–6% per extra competitor on price). The Latvian government is reforming the Public Procurement Law in 2026 — most visibly, raising the regulated threshold from €10k to €143k. But the reform is essentially a guess about why suppliers don’t bid. Jāzeps’s thesis asks the prior question: why do legally eligible suppliers stay away, especially in knowledge work (IT, consulting, R&D, analytics)? (#DWCV8W, #U57A7C)

The theoretical move. He reframes a tender as a signaling environment: the buyer designs a screening architecture, and submitting a bid is a costly signal. The supplier’s bid/no-bid decision is then a rational response to perceived transaction costs (PTC) that hit before any contract is awarded. He decomposes PTC into three components: bid preparation burden, evaluation uncertainty, and integrity risk — and builds a “two-gate” participation model (Gate 1 = is this tender even worth engaging with; Gate 2 = can I produce a competitive bid at acceptable cost). Knowledge services amplify all of this because the bid itself requires expensive intellectual labour you can’t recover if you lose. (#YFYYHE, #3T92HU)

The empirics. Sequential explanatory mixed-methods (#U733DL):
1. Quantitative phase — IUB open procurement data 2024–2025, KIBS tenders identified by CPV codes (72/73/79) plus a Latvian-language keyword dictionary as robustness check. Regressions (Poisson/NB for bidder counts, logit for single-bid). The trick: he uses residuals diagnostically. Where the model explains low participation well → cost-binding sub-market. Where actual participation is way below predicted → something the observable variables can’t see (trust, evaluation competence concerns) — and those become the targets for interviews. (#44QV8L, #6CWVF5)
2. Qualitative phase — semi-structured interviews with suppliers (domestic + international, active + withdrawn), using anonymised tenders from the dataset as vignettes, coded against the gate framework. (#82JSLX)
3. Synthesis — a sub-market × binding-constraint matrix (cost-binding / quality-binding / integrity-binding / mixed), used to predict which sub-markets the 2026 reform will actually reach. The reform mostly simplifies formal procedural cost; if a sub-market is informally constrained (trust, integrity), threshold changes won’t help. (#AG5337)
Cross-cutting variable: supplier origin. Cross-border participation in Latvian KIBS is 1.9% vs. Estonia’s 13.6% — same EU rules, very different behaviour, suggesting informal participation costs. (#5M8N7W)

The LLM usage

This is in §3.2 (research design) and is fairly disciplined. The IUB export gives titles and basic fields, but the real gate-model variables (evaluation criteria weights, qualification threshold specificity, documentation complexity, required annexes, award records) live inside PDFs and .doc files, mostly in Latvian, with formatting that varies by contracting authority. Manual coding the full sample (~1,789 tenders) is infeasible, so he uses Claude Haiku via API for structured extraction.

The protocol has four stages (#8LUL7H, #QTUWVU):
1. Manual gold-standard set — he hand-codes a reference set first.
2. Prompt + schema development — Claude Haiku with structured output schemas, iterative prompt refinement against the gold standard. He cites Lunn et al. 2025 for the iteration method, and Konet et al. 2024 / Schmidt et al. 2024 as evidence that LLMs can hit ~80–96% accuracy on structured extraction when guided by a predefined schema and validated. (#C7RMKD)
3. Full pipeline run with audit logging.
4. Validation on a random 20% subsample against source documents, with agreement rates reported per variable (Krippendorff’s principle: reliability isn’t a single number, it varies by what you’re extracting). Variables below threshold are either re-prompted or dropped with the limitation flagged.
He’s explicit about the measurement-uncertainty caveat in the limitations (#NUMYCT). The framing is conservative: LLM as a force-multiplier on a Krippendorff-style content-analysis protocol, not as an oracle — gold standard first, schemas constrain output, human validation per variable, and the methodological literature he leans on is the recent computational-content-analysis wave rather than anything hand-wavy.

So in one breath: theory = tenders are screening architectures, non-participation = rational response to perceived transaction costs; method = mixed-methods with regression residuals targeting the interview phase; LLM role = scaling structured variable extraction from Latvian tender PDFs under a validated content-analysis protocol; payoff = a sub-market typology that predicts which slices of the 2026 reform will actually move participation.

Prep window	n	avg bidders	single-bid %
≤15 days	252	2.65	38.9%
16–25 days	637	2.57	38.0%
26–40 days	362	3.73	24.3%

CPV div	n	avg bidders	single-bid %
72 (IT services)	216	2.27	50.5%
73 (R&D)	70	2.14	41.4%
79 (business/consulting)	955	3.06	30.5%

Untitled thesis

Findings — bid/no-bid signals in the gold-standard sample (n=1,278 KIBS tenders)

Verification of headline tables in §2.3 (#7NBHCY)

Data quality flags — these affect the regression in #3NHBCQ

What is currently runnable, and what it shows

Three concrete edits I’d make to the draft

Two analyses I would queue next if extraction can be completed

The thesis in plain English

The LLM usage