The Hard Teacher’s Leadership Coping to the COVID-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most teachers in Mexico are not experts on Information and Communication Technologies, some rural areas lack a good internet connectivity or even electricity. This context led us to determine: How can teachers keep the pace of educational leadership? and How they cope their teaching task with the COVID-19 pandemic? The sample included 329 teachers from urban and rural zones, 71.1% female and 28.9% male, with a mean age of 38.8 years, working in public (71.7%) and private (28.3%) schools. A self-evaluation template was used to assess the planning, didactical sequence analysis and evaluation competence from the teachers. Our aim was to sketch a teacher’s leadership competences profile, specifically in these pandemic times. The results showed than 75.7% of the teachers had an internet access between Good and Very good; on the contrary, 78.4% of the teachers considered that most of their students had between “not very good” to “very bad” internet access. Only a few teachers addressed the didactic planning or followed its development and assessment: I have elaborated and shared with the students indicators of achievement from the didactical sequence (32.8%); I have stimulated processes of reflection upon learning through an instrument (22.5%); I have regularly incorporated and used digital tools and Internet (31.9%); at last, I have established and conducted moments of evaluation, self and formative co-evaluation in which the students have been able to make changes based on the feedback received (30.1%). However, teachers are coping with this pandemic time and it may involve a change in educational strategies towards the future.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it