Effect of Linguistic Differences and Formal Training on Scholarly Productivity
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".