The Return of the Lockout in Australia: a Profile of Lockouts since the Decentralisation of Bargaining
Bibliographic record
Abstract
"Virtually unheard of outside the struggles of unions to establish themselves in the\n1880s-90s and the Great Depression1, lockouts have resurfaced in a series of disputes\nsince the decentralisation of bargaining during the 1990s. A quantitative profile of\nlockouts during the past decade of enterprise bargaining is presented as the first phase\nof a project which examines lockouts in Australia. Lockouts are still rare, but the\nnumber of working days lost in disputes with lockouts was almost six times greater for\nthe second half-decade of enterprise bargaining than the first half-decade. Moreover,\nlockouts accounted for over half of the long disputes (i.e. over a month). Lockouts are\nespecially common in manufacturing (though all major ANZSIC categories have had\nat least one lockout), where they constituted one quarter of all working days lost to\nindustrial disputes in the second half-decade of enterprise bargaining. Indeed, working\ndays lost to industrial disputes in manufacturing would have fallen but for the rising\nuse of lockouts. Other data are presented showing that lockouts are most common in\nVictoria, disproportionately common in regional areas and used primarily either to\nrepel union bargaining demands, coerce employees into signing AWAs or as a tool for\nconcession bargaining."
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".