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Record W2346162782 · doi:10.5267/j.msl.2016.4.004

Detecting privatization factors on promoting exports of steel companies

2016· article· en· W2346162782 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement Science Letters · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessIndustrial organization

Abstract

fetched live from OpenAlex

Privatization is considered as the transfer of business from the government to the private sector. This process is often accomplished in an attempt to reduce the size of government, increase the productivity and export activities. There is no doubt that in an export oriented economy, government tries to promote expert based activities by making necessary changes on rules and regulations. This paper presents an empirical investigation to determine important factors on promoting export in Iran. The study designs a questionnaire in Likert scale and distributes it among some experts active in steel industry. Kaiser-Meyer-Olkin Measure of Sampling Adequacy is equal to 0.839 and Bartlett's Test of Sphericity yields an approximation Chi-Square value of 2485.02 (Sig. = 0.000, df = 276). Using principle component analysis with Varimax rotation, the study has detected five important factors including human resources development, productivity management, marketing management, creating competitive environment and building necessary infrastructures, which influence the most on export activities.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.260
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.213
Teacher spread0.193 · 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