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Record W4293147109 · doi:10.51952/9781847421463.ch011

Having the time for our life: re-working time

2006· book-chapter· en· W4293147109 on OpenAlexaboutno aff
Linda Boyes, Jim McCormick

Bibliographic record

VenuePolicy Press eBooks · 2006
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsWorking timeHistoryEngineeringWork (physics)Mechanical engineering

Abstract

fetched live from OpenAlex

How could working lives be reshaped to afford all employees greater choice in achieving work–life balance? The Scottish Council Foundation’s ‘Lifelines’ study has considered this question, drawing on comparative findings from policy and practice in the UK, Canada and Australia and testing practical options for reform with public and private sector employees in Scotland (Boyes and McCormick, forthcoming b). Two related dimensions for improvement emerge. One involves the context and culture in which work is performed, covering pay and conditions (for example, holiday and sick leave entitlements), employee representation and participation, and the balance between contribution and reward. Making progress towards more employees having a more fulfilling experience of work will involve a decisive shift from dominant measures of economic progress, such as aggregate employment rates, and a traditional agenda for health and safety at work, to a deeper understanding of the true determinants of job satisfaction, motivation and morale. The second set of issues is a sub-set of the first, with a focus on a ‘whole career’ approach to flexibility at work, including reform of time across working life. This chapter focuses on the second of these. We have explored ways in which ‘time assets’ could be accrued, drawing on concepts of saving, borrowing and buying periods of leave. Employees can achieve greater integration between work and other aspects of their lives by accumulating assets in the shape of enhanced periods of paid leave (using deferral of both existing leave entitlements and salary) and by being enabled to take a phased approach to retirement.

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)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.015
Scholarly communication0.0140.024
Open science0.0020.009
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0120.003

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 head0.287
GPT teacher head0.421
Teacher spread0.134 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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