Framing Expertise: R. Alan Eagleson and the Toronto <b> <i>Globe and Mail</i> </b> , 1967–1994
Bibliographic record
Abstract
R. Alan Eagleson, Executive Director of the National Hockey League Players’ Association from 1967 to 1992, had a major impact on the business of hockey. Although he was suspected of conflicts of interest, questioned for unethical behaviour towards athletes and was eventually indicted by the FBI in 1994 on multiple charges, Eagleson remained in his position of power over 20 years. This study used media framing to reveal that Eagleson’s expertise was one of the origins of his extensive power and why he was seen as the best candidate to protect and advocate for the rights of NHL players. The expertise frame was the only frame to emerge in newspaper coverage in Toronto’s The Globe and Mail consistently throughout Eagleson’s tenure, even when his power was challenged. However, there was a noticeable decrease in the use of the expertise frame once the FBI investigation was made public in 1991. This study revealed that a finite number of journalists deployed the framing elements, and the perception of his expertise may have played a role in how he was able to maintain power and influence in the sport for so long, despite allegations of conflicts of interest and criminal activity.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".