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Organisational and Managerial Careers: A Coevolutionary View

2020· book-chapter· en· W3084261470 on OpenAlexaff
Hugh Gunz, Wolfgang Mayrhofer

Bibliographic record

VenueOxford University Press eBooks · 2020
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMentorshipPerspective (graphical)Field (mathematics)SociologyContext (archaeology)Space (punctuation)Career developmentEpistemologyPolitical scienceGeographyComputer sciencePedagogyArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Abstract The field of organisational and managerial careers (OMC) covers a broad range of approaches, with roots in fields ranging from sociology to vocational and developmental psychology. This chapter draws on recent work that proposes a framework (the Social Chronology Framework, SCF) in which the study of careers, in particular OMC, is seen to involve the simultaneous application of three perspectives, to do with being, space, and time. Building on this, the SCF takes a view that emphasises the importance of a coevolutionary perspective. Within a bounded social and geographic space, career development happens based on configurations of individual and collective career actors who provide context for each other and coevolve together. The chapter illustrates this by showing how the SCF can suggest new approaches to studying established career development arrangements, such as mentorship.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.008
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.182
Teacher spread0.158 · 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 designTheoretical or conceptual
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

Citations4
Published2020
Admission routes1
Has abstractyes

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