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Record W4290999283 · doi:10.5539/hes.v12n3p155

How the Academic Profession is Perceived in Public Technological Universities in Mexico

2022· article· en· W4290999283 on OpenAlexvenueno aff
José Luís Arcos Vega, Marja Johana López Quintero, Marcos A. Coronado, Marissa López Paredes

Bibliographic record

VenueHigher Education Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryPublic institutionHigher educationPreferenceInstitutionProductivityPublic relationsWork (physics)Public sectorPublic universityPrivate sectorPolitical scienceJob satisfactionSociologyMedical educationPublic administrationManagementEconomic growthSocial scienceEconomicsMedicineEngineering

Abstract

fetched live from OpenAlex

In this descriptive study, we utilized the national database that was obtained from the international APIKS, Academic Profession in the Knowledge – Basic Society survey, where 3,776 Mexican professors participated from the different public research centers, federal public institutions, state public institutions, technological public institutions, as well as from private institutions. This research emphasizes on the analysis of the public technological institution subsystem, in the variables of gender, preference between teaching and research contract situation, salary, labor and salary, professional environment, that are part of the elements composing the instrument applied to the professors working in higher education institutions. One of the purposes is to find out more about productivity, degree of satisfaction about the conditions and academic work nationwide.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.100
GPT teacher head0.413
Teacher spread0.314 · 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.

Study designQualitative
DomainIncentives
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

Citations1
Published2022
Admission routes1
Has abstractyes

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