Bibliographic record
Abstract
This study investigates the impact of political connections on the awarding of government contracts to Canadian companies. Two-stage least squares (2SLS) analyses were performed on a sample of S&P/TSX companies from 2010 to 2014 inclusively. The results show that political connections are positively and significantly associated with the winning of government contracts. Politically connected firms obtain more government contracts and higher contract values than non-connected firms. Political connections thus appear to be directly associated with securing government contracts in the Canadian context. Firms can view political connections as a non-market, long-term strategy to help them gain competitive advantages and improve their performance. Accordingly, they tend to appoint directors and managers taking these connections and the advantages that can be gained into account. However, they must seriously consider the cost-benefit ratio of this strategy. For example, the costs incurred could be ethical in nature and firms could find themselves in a position of conflict of interest that could lead to extensive negative media exposure. These results alert regulatory and governmental organizations to the need for them to remain vigilant and to strengthen corporate governance regulations to prevent the excesses and abuses that could arise from firms’ political actions. Our study is the first to demonstrate a direct relationship between corporate political connections and government contracts in the Canadian context. The results confirm the growing interdependence between politics and business, particularly the increase in the number of corporate actions intended to influence government decisions.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".