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Social Consequences of Employee/Management Buyouts: Two Canadian Examples From the Forest Sector*

2002· article· en· W2064535492 on OpenAlexaffabout
Thomas M. Beckley, Naomi Krogman

Bibliographic record

VenueRural Sociology · 2002
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of AlbertaUniversity of New Brunswick
Fundersnot available
KeywordsReal estateBusinessLand tenureEstateLocal communityPopulationNatural resourceEconomic growthEconomicsFinanceSociologyAgricultureGeography

Abstract

fetched live from OpenAlex

Abstract Local control of natural resource processing facilities in small rural communities is often viewed as beneficial to community development. This paper employs social impact assessment tools to examine the social and economic effects of change in the ownership of forest products mills in two communities. Our analysis documents (1) the degree to which local ownership of the new, locally owned corporations led to local reinvestment of profits, and (2) whether the goals of the architects of these buyouts were realized: the maintenance of jobs, income, population, and a way of life. Overall, both communities were able to maintain jobs, population, and real estate values, and profits were reinvested in mill upgrades. After the buyouts, however, both communities experienced a rise and then a decline in community cohesion, and changes in local social and power relations, in which local ownership was short‐lived; benefits to relationships within the community were mixed.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.317

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0210.005
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.228
Teacher spread0.196 · 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

Citations10
Published2002
Admission routes2
Has abstractyes

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