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Record W2261529737

Can Government Workplaces Be Made World-Class?

2010· article· en· W2261529737 on OpenAlexaffabout
Zsuzsanna Lonti, Sara Slinn, Anil Kumar Verma

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsAdversarial systemGovernment (linguistics)RestructuringCollective bargainingJurisdictionPolitical sciencePublic relationsPublic administrationDispute resolutionIndustrial relationsBusinessLaw
DOInot available

Abstract

fetched live from OpenAlex

Citing empirical evidence drawn from an extensive survey of managers in the federal jurisdiction and in Ontario, Alberta, Nova Scotia and Manitoba, the authors report major shifts in the organization of work in Canadian government workplaces, marked by the widespread adoption of innovative practices. The same survey indicates that unions have had little involvement in the restructuring process, a fact which is attributable to the largely adversarial nature of labour-management relations in the 1990s, a highly centralized paradigm of collective bargaining, and the resistance of union leaders to change. As the authors note, however, attempts to introduce innovations are not sustainable without meaningful union participation. In the second part of the paper, the authors assess whether the recommendations of the Fryer Committee, if implemented, would foster the adoption of innovative practices and enhance the productivity of government workplaces. Many of the Committee’s proposals, in their view, are likely to eliminate unnecessary barriers to interaction between labour and management, promote cooperative problem-solving and communication, and streamline needlessly complex dispute-resolution and industrial relations procedures. The authors conclude, however, that the recommendations do not go far enough, and they are critical of the Committee’s failure to address concretely the government’s tendency to exploit its dual role as employer and legislator by interfering in collective bargaining disputes.

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)0.004
metaresearch head score (Gemma)0.014
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: none
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0120.013
Scholarly communication0.0170.017
Open science0.0020.007
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0190.003

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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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
Published2010
Admission routes2
Has abstractyes

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