Bibliographic record
Abstract
The existence of teachers working under fixed-term contracts as bearers of school education cannot be overlooked. The Teaching and Learning International Survey 2018 (TALIS 2018), conducted by OECD, reported that nearly a quarter of teachers in lower secondary schools in Japan today are fixed-term teachers, and the number has been significantly increasing. Despite this reality, the situation of fixed-term teachers has not been accurately addressed regarding policy makers. Previous teacher studies show them as marginalized individuals within the profession. The limited research on this group does little to capture the reality of their situation as previous studies have focused more on qualitative methods than quantitative. In response to this, this study aims to reveal a holistic picture of fixed-term teachers’ conditions, adapting a quantitative method using the TALIS 2018. In doing so, this research does not treat fixedterm teachers as a monolithic entity, but rather as an entity that includes internal variety. Specifically, I classify fixed-term teachers according to their attributes (gender and age) and working situations (work status, working hours, and sense of burden on the job), and examine their actual conditions (awareness of teachers, job satisfaction, and relationship with their surroundings). The result of this analysis reveals that fixed-term teachers are a diverse group including great internal variety, and that differences in age and workload, for example, cause differences in one’s sense of job satisfaction. This indicates the necessity of examining the internal diversity of fixed-term teachers. In future research, it is necessary to clarify the actual situation of fixed-term teachers while also considering other possibly related factors such as academic subject matter and family circumstances.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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 teacher head, 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".