MétaCan
Menu
Back to cohort
Record W2185712647 · doi:10.25071/1705-1436.137

Can Joint Training Increase Union Knowledge and Power?

2005· article· en· W2185712647 on OpenAlexvenueno aff
Corliss Olson

Bibliographic record

VenueJust Labour · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringGlobalizationLeverage (statistics)Training (meteorology)Power (physics)Economic systemBusinessWork (physics)Labour economicsEconomicsPolitical scienceEngineeringMarket economyComputer scienceFinanceArtificial intelligence

Abstract

fetched live from OpenAlex

Globalization has been accompanied by a decline in unionization; however, while globalization presents extremely serious challenges to unions, globalization does not necessarily result in weakened unions. It is important for unions to identify and utilize ways to increase and leverage union power that are responsive to the pressures of globalization. Companies frequently introduce training during restructuring efforts aimed at remaining competitive in a global environment. This paper describes a joint union-management training program which offers an example of a pro-active union approach to joint training initiatives. The training took place in early 2004 in a typical paper mill in central Wisconsin. While the training was designed and undertaken in response to various competitive pressures, the content of the training was primarily determined by employee focus groups. The training design is examined against criteria for successful union involvement in joint ventures. The paper argues that, while joint ventures typically address management's production concerns at the expense of labor, a pro-active union can work to ensure that benefits also accrue to the union. Recent literature on union power in a globalized economy suggests that this training model could be used in other industries to enhance union knowledge and power.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.304
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJust LabourSame topicLabor Movements and UnionsFrench-language works237,207