Investigating the Relationship between Iranian EFL Teachers’ Autonomy and Their Neuro-Linguistic Programming
Bibliographic record
Abstract
The present study was an attempt to investigate the relationship between English Language Teachers’ autonomy and their Neuro-linguistic Programming (NLP). To this end, a group of 200 experienced English language teachers at various language schools in Tehran, inter alia, Asre Zaban Language Academy, were given two questionnaires namely Teaching Autonomy Scale (Pearson & Moomaw, 2005); and Reza Pishghadam’s Neuro-linguistic Programming Questionnaire (2011) among which 162 instruments were returned. After being verified, 129 questionnaires which had been thoroughly completed were selected and carefully examined. At the outset of the data analysis stage, the researcher was supposed to pick out the accurate correlational measure. In this effort, assumptions of linearity of the relationship between the variables and normality of the data were verified and, as a result, Spearman rho was employed. The findings of an in-depth data analysis revealed that the null hypothesis of the study was to a large extent supported. That is to say, exclusive of General autonomy which was positively and significantly related to NLP, other sub-categories of autonomy—Curriculum and Total, were not correlated significantly with NLP.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".