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Record W4407633663 · doi:10.1111/rego.12658

“Is Lobbying for Losers?”: Corporate Behavior and Canadian Military Procurement Contracting

2025· article· en· W4407633663 on OpenAlex
Andrea Migone, David Chen, Bryan Evans, Alexander Howlett, Michael Howlett

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRegulation & Governance · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsProcurementBusinessAccountingPolitical scienceEconomic policyMarketing

Abstract

fetched live from OpenAlex

ABSTRACT Lobbying is a multi‐faceted phenomenon that involves interest groups and corporations contacting politicians and officials in order to try to achieve their policy preferences. While interest group policy‐related lobbying has received a great deal of attention, studies of corporate contract lobbying are rarer even though this is a much older phenomenon. The article critically examines the commonly‐held position that in the latter case “lobbying is for winners”; that is, that large scale corporate lobbying helps secure contracts that might otherwise have gone to a different firm. It argues instead that firms enjoying technological and other market‐related strengths enjoy an “insider advantage” and lobby less than firms in more competitive situations. In other words that in many situations “lobbying is for losers,” a tool used by weaker firms trying to match or offset the technological and other advantages enjoyed by dominant firms. The article draws on government lobbying registers to examine recent defense‐related procurement efforts in Canada to purchase fighter jets, naval surface ships, patrol vessels, and search and rescue aircraft and the contract lobbying they engendered. Evidence from the four cases provides support for the “loser” thesis with respect to large‐scale technologically advanced goods but also the need to carefully define what constitutes an “inside advantage” allowing firms to forego or delay their lobbying activity, often until only after a contract has been awarded.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.251
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it