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Record W7130653146 · doi:10.3828/hsir.2025.46.6

Sir Peter D. Carr: Building a Career in an Industrial Relations ‘Golden Age’. An Interview by Greg J. Bamber

2025· article· en· W7130653146 on OpenAlexaboutno aff
Greg J. Bamber

Bibliographic record

VenueHistorical Studies in Industrial Relations · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsConciliationIndustrial relationsApprenticeshipArbitrationCommissionWork (physics)Trade unionWorking life

Abstract

fetched live from OpenAlex

In this edited interview, Peter Carr summarized his working life: from woodworking apprentice in the 1940s and his work on construction sites (in the mid-1950s) to being appointed Labour Attaché at the British Embassy in Washington (1978–82) while still holding membership of his construction trade union. In between, there are his spells in adult education – Fircroft College, Birmingham, and Ruskin College, Oxford – which he funded himself, before training as a college lecturer and then pioneering shop-steward training in Halifax, Yorkshire, and at Thurrock, Essex. He then worked part-time at the National Board for Prices and Incomes, before playing a leading role in the Commission for Industrial Relations and its successor, the Advisory, Conciliation and Arbitration Service. After Washington, he was recruited into a series of high-profile, public-sector positions, initially in the Employment Department, then in the Health Department, where he worked part-time into his eighties. Carr’s life illustrates the informal way in which well-connected people could move into jobs at different levels. In that sense he was a product of his time, being particularly adept at cultivating networks in the labour movement world and the industrial relations community (when unions had more influence than in later periods). In his post-Washington years, his success reflected his hard work, networks, competence, and adaptability. This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licenses/by-nc-nd/4.0/ .

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)0.012
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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.005
Scholarly communication0.0060.009
Open science0.0010.003
Research integrity0.0070.012
Insufficient payload (model declined to judge)0.0160.004

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 head0.212
GPT teacher head0.388
Teacher spread0.176 · 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
Published2025
Admission routes1
Has abstractyes

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