MétaCan
Menu
Back to cohort
Record W2107840308 · doi:10.1177/0021886310367945

Ideology, Crisis Intensity, Organizational Demography, and Industrial Type as Determinants of Organizational Change in Kibbutzim

2010· article· en· W2107840308 on OpenAlexaff
Zachary Sheaffer, Benson Honig, Abraham Carmeli

Bibliographic record

VenueThe Journal of Applied Behavioral Science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCooperative Studies and Economics
Canadian institutionsMcMaster University
FundersAriel University
KeywordsIdeologyOrganizational changeOrganizational commitmentDemographic economicsFinancial crisisSociologyPolitical scienceEconomicsPublic relationsPolitics

Abstract

fetched live from OpenAlex

Kibbutzim were a pure missionary organization known for their egalitarian—communal lifestyle. However, like many other organizational forms, the kibbutz model has been subjected to significant pressures to become more market oriented. This challenges the existence of kibbutzim in many ways. Stressing ideological homogeneity as a key predictor of change, this study also examines the effect of crisis as assessed by financial distress, demographic depletion, and type of manufacturing industry, on change intensity. Using a sample of 171 kibbutzim over a 7 year-period, the findings indicate consistent effects of ideology, crisis intensity, demographic depletion, and organizational size on change intensity. Theoretical implications for atypical organizations are discussed.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.281
Teacher spread0.238 · 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 designObservational
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

Citations6
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Applied Behavioral ScienceSame topicCooperative Studies and EconomicsFrench-language works237,207