“Is Lobbying for Losers?”: Corporate Behavior and Canadian Military Procurement Contracting
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".