Looking for Competent School Leaders for Indigenous Schools: The New System to Appoint School Leaders in Mexico
Bibliographic record
Abstract
The understanding that leadership matters is well regarded in many types of organizations not only in education. In 2015 Mexico implemented a new system to appoint school leaders updating the previous, which was applied for more than four decades. This system aims to appoint the most competent candidate as school principal based on the scores they get on two tests. This study explored how the new system enhances or hinders preparation and readiness for leadership positions, and the effectiveness of tutoring and in-service professional development. Five newly appointed school leaders to Indigenous schools were followed throughout their first year of service. They were interviewed at the beginning, after six months, and at then end of their first year. Thematic analysis was used to process the data gathered from semi-structured interviews using a selective coding approach. Two main predefined themes were explored in this study: Leadership Preparation and Tutoring and Professional Development. Findings indicate that for schools located in remote Indigenous communities, isolation and the lack of communication infrastructure, such as internet and phone signal, hinder the possibility of effective training and tutoring.This study concludes that even though the new system seems to have made progress in appointing better school leaders, it is only partial since aspiring leaders are neither required to make specific preparation for their new post nor offered these opportunities, hindering their readiness to enact headship effectively.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".