ANALYSIS OF THE GOVERNMENT RELATIONS BETWEEN CREDIT UNIONS AND THE FARM CREDIT CANADA
Bibliographic record
Abstract
Since the 1990s, some credit unions have expressed concerns about Farm Credit Canada’s (FCC)—a government-owned agricultural lender— competitive behaviour, potentially risking the viability of credit unions that specialize in serving the agricultural market. Given this historical tension, the important role that credit unions play in the Canadian economy, and the limited scholarly attention to credit unions’ government relations, this research explores the evolving relationship between credit unions and FCC over the past 50 years from the vantage point of its advocacy strategy. In conducting this research, I employed a qualitative case study method. I reviewed 50 FCC annual reports and conducted 13 interviews with officials from credit unions, the Canadian Credit Union Association (CCUA), and FCC. To describe the evolving relationship, I identified five distinct periods based on the prevailing nature of the credit union sector’s government relations strategy concerning FCC: 1) 1969-1992, Pro-complementarity, 2) 1993-2000, Shifting towards Pro-competition, 3) 2001-2010, Pro-Competition, 4) 2011-2021, Pro-Competition with the establishment of the Liaison Committee, and 5) 2022-onward, Competition with Partnership. In analyzing these five periods, I also explain how credit unions have engaged in policy advocacy (outside-in lobbying) and policymaking (inside co-design) to protect their business and purpose against intrusions from FCC. Policy advocacy played a predominant role during the second and third periods as FCC exhibited aggressive competitive behavior. In contrast, policymaking has been more prominent in the last two periods, as the Liaison Committee served as a policy network that promoted collaboration and fostered joint initiatives.
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.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".