First National Maintenance Corp. v. National Labor Relations Board: Eliminating Bargaining for Low-Wage Service Workers
Bibliographic record
Abstract
The Supreme Court decision finds an employer privileged not to bargain with the union over a decision to eliminate a portion of operations (by not renewing a contract with a particular customer), undertaken entirely for economic reasons turning not at all on labor costs, and without animus to the union. No such case has ever been presented to the National Labor Relations Board, and interviews with the principals reveals that these were not the facts of First National Maintenance either. The case was a carefully-constructed hypothetical that omitted key facts, such as the employer's history of illegal conduct to avoid recognizing the same union at other locations. Subsequent Board cases reveal that all real-world examples of employers that refuse to bargain over downsizing, also involve some combination of union animus, planning to do the work at another location, or attempts to avoid bargaining over clearly bargainable issues. The application of FNM to such cases remains conjectural. The drafting of the opinion is traced through Supreme Court memoranda and drafts and reveals no clear decision on the issues that actually arise. Issues that arise today involving union representation of janitors arise outside the FNM framework, notably whether to follow Canadian practice and typically find janitors employed, jointly or individually, by the owners of the building they clean.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".