MétaCan
Menu
Back to cohort
Record W2036301801 · doi:10.1177/0021886300362003

Managers as Evaluators

2000· article· en· W2036301801 on OpenAlexaff
Barbara Schneider

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInterviewConstruct (python library)InstitutionPositivismSociologyProcess (computing)PsychologyPublic relationsWork (physics)PedagogyPolitical scienceSocial scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

This article examines how managers construct their version of the organization as a stable and objective reality and in doing so accomplish their work as managers. Two senior managers in an educational institution evaluated a group of their educational programs by interviewing all the teachers in the programs. The managers invoked traditional, positivist ideas of interviewing both for themselves and for the interviewees. However, analysis reveals the interviews not simply as opportunities for knowledge to be transmitted from one person to another but rather as interactional accomplishments in which the interviewer is deeply implicated in the production of answers. The managers, nevertheless, regard the answers as the result of an objective research process and use them as the basis for a report that promotes their view of the organization as the reality of organizational life, legitimated by evidence provided by the teachers themselves.

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)0.035
metaresearch head score (Gemma)0.058
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 score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.058
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.006
Scholarly communication0.0110.007
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.003

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 head0.020
GPT teacher head0.267
Teacher spread0.246 · 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

Citations12
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Applied Behavioral ScienceSame topicManagement and Organizational StudiesFrench-language works237,207