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Record W7000070681

Effect of Linguistic Differences and Formal Training on Scholarly Productivity

2020· article· en· W7000070681 on OpenAlexaboutno aff

Bibliographic record

VenueJournals & Books Hosting (International Knowledge Sharing Platform) · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmProductivityProduct (mathematics)Training (meteorology)Formal educationHigher educationSignificant difference
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to find out the effect of linguistic differences and formal training on scholarly productivity among lecturers in Administrative Sciences. Data were obtained from 176 faculty members in Administrative Sciences drawn from 11 universities in Quebec Canada. Also, personal interviews were held with the deans and directors of research in seven of the eleven universities. The result of our findings showed statistically significant difference in total book production between those with working knowledge in both French and English. And those with working ability in only French or English. There was no significant difference in total article production between those with working ability in both French and English and those with working knowledge in only French or English. There was no significant relationship between the extent to which research is encouraged in the faculty in which the faculty members had their graduate training and scholarly productivity. Based on these findings, it was concluded that linguistic ability affects scholarly productivity. Also, commitment to scholarly activity cannot be enforced. It must come as a product of the enthusiasm that a faculty member feels toward his or her job. The implications for this study along with some directions for further study are addressed. Keywords: linguistic ability, formal training, scholarly productivity. DOI: 10.7176/EJBM/12-23-12 Publication date: August 31 st 2020

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 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)0.001
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.298
Teacher spread0.194 · 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 teacher head, not a consensus.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueJournals & Books Hosting (International Knowledge Sharing Platform)Same topicLinguistics, Language Diversity, and IdentityFrench-language works237,207