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Record W2275151200 · doi:10.14288/1.0093333

Bargaining strategies of white-collar workers in British Columbia

2011· article· en· W2275151200 on OpenAlexaboutno aff
Maureen Patricia Marchak

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsCollarWhite (mutation)Labour economicsBusinessEconomicsFinance

Abstract

fetched live from OpenAlex

The primary objective of this thesis is to examine the relationship between job control — that is, the amount of discretion a worker exercises at his job — and bargaining strategies. The relationship between income and bargaining strategies is also examined, and the joint effects of income and job control are analysed. In addition, attention is given to the association between social interaction rates among workers with job control levels held constant, and bargaining strategies. The main argument associates job control with replaceability and with marketability of skills; these with bargaining strategies; and, consequently, job control with bargaining strategies. Hypotheses are stated which link low job control to the low incidence of individual bargaining, low income, willingness to join unions, and union membership. An argument then links low job control to passive behavior, and consequently to low individual bargaining, and low rates of participation in union activities. Survey research, involving interviews with white-collar workers in 43 commercial firms in British Columbia, was undertaken to test the arguments. Tests consisted of percentage comparisons between workers with differing levels of job control, with respect to specific questions and responses. Data was examined separately for men and women. Support was found for the predicted associations between job control and individual bargaining, and job control and Income. For women, but not for men, support was found for the predicted associations between job control and willingness to join unions, and job control and union membership. For men, but not for women, limited support was found for the predicted relationship between job control and participation rates in union activities. An analysis of the relationship between income and strategies revealed that low incomes are associated with willingness to join unions. When job control levels are held constant, income continues to be inversely associated with pro-union responses. Similarly, when income levels are held constant, an inverse relationship is maintained between job control and pro-union responses. High income tends to decrease the effects of low control, and high control tends to decrease the effects of low income. The two variables also interact, such that a combination of low control and low income is strongly associated with pro-union responses. It is suggested that the evidence justifies further examination of relationships between job control and bargaining strategies, but that this examination should take into consideration more detailed information regarding specific populations engaged in given skill areas, and the employment opportunities available to them. An additional argument associates low interaction rates of workers and management personnel with pro-union responses and union membership, and high interaction rates of workers and co-workers with pro-union responses and membership. The argument is stated with respect to the opportunity workers have for engaging in discussion of bargaining positions, defining the employer as an opponent, and organizing collective energies. This section of the theory was generally unsubstantiated. It is suggested that white-collar workers have higher interaction rates than manual workers, and differences in rates do not have a substantial influence on organization potential.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.015
GPT teacher head0.199
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2011
Admission routes1
Has abstractyes

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