MétaCan
Menu
Back to cohort
Record W2290940011

The Employment Equality (Age) Regulations and Beyond

2006· article· en· W2290940011 on OpenAlexaboutno aff
George W. Leeson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsAge discriminationPromotion (chess)Context (archaeology)Demographic economicsMandatory retirementBusinessPolitical scienceLabour economicsPsychologyEconomicsLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we consider the implementation of the employment (age) regulations in the United Kingdom in both an historical and an international context, drawing on evidence from the United States, Australia, Canada, New Zealand and Ireland. The workplace is not the only setting in which age-based discrimination makes itself felt, but research does indicate that employment provides the most common ground of age discrimination complaints and that age discriminatory practice affects older age groups more than younger age groups. Survey evidence does indicate that many employers regard older workers as equal to if not preferable to younger workers in a number of key employment and productivity areas, while other evidence suggests that age does have an influence on the recruitment decisionmaking process. Age is a factor in the recruitment process and few employers have mechanisms to encourage older workers to apply for jobs, even though many have a formal written policy on equal opportunities (also in respect of age). Older workers are also less likely to be offered job-related training and education. Thus, chronological age does figure in the workplace and in employers’ policies and practices in relation to recruitment and promotion, access to training, retirement, and redundancy. These policies and practices influence the way in which older employees plan for withdrawal from the labour force. The implementation of the employment equality (age) regulations as of October 2006 indeed changes practice and policy in the workplace, but does not signal the end of retirement, and international evidence would indicate that only a small proportion of agerelated cases are taken to tribunal, with an even smaller proportion being upheld there.

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: none
Teacher disagreement score0.630
Threshold uncertainty score0.995

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 studies0.0010.001
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 head0.173
GPT teacher head0.423
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 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

Citations14
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicRetirement, Disability, and EmploymentFrench-language works237,207