The Medium and the Anti-Union Message: Forced Listening and Captive Audience Meetings
Bibliographic record
Abstract
Employer captive audience meetings (CAMs) are a rare example in which people in a democratic society are forced to listen to opinions of others with which they may strongly disagree. Employees are not chained to a post, but they are nevertheless economically compelled to listen to their employer's anti-union opinions. The uniqueness of being compelled to listen makes the CAM a powerful signaling device through which the of economic vulnerability is transmitted to employees. The medium (CAMs) is its own message, and it should be regulated as such. The author explores the extent to which this approach is reflected in current labor law, and finds that the principle approach to CAMs in Canadian labor law is to treat CAMs as message neutral event that can color the content of the speech made in the meeting. He argues for an approach that treats the CAM as an independent signaling device. This approach would refocus the labor boards' attention on the question of whether CAMs interfere with the formation of unions, and whether permitting employer CAMs advance sound labor policies that are consistent with the values underling the Charter of Rights and Freedoms.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".