The Relationship Between Possible Self of the Social Studies and History Teacher Candidates and the Attitudes Towards Teaching Profession
Bibliographic record
Abstract
This study was conducted to determine to what extent the attitudes of pre-service teachers of social studies and history towards the teaching profession may predict possible teacher selves. Participants of the research are senior social studies teacher candidates studying at a public university in the spring semester of 2018-2019 academic year and history teacher candidates registered in the pedagogical formation program. Relational survey model, one of the survey models, was used in the research. The data was collected through Teacher Candidates Possible Selves Questionnaire and Attitude Scale towards Teaching Profession. The data obtained from the study were analyzed by Independent samples t test and simple linear regression analysis techniques. According to the results obtained from the research, there was no difference between the attitudes of social studies teacher candidates and history teacher candidates who received pedagogical formation education towards the teaching profession. While the department of teacher candidates was not effective on the expected teacher selves, it was determined that it was effective on the feared teacher selves. It has been understood that the attitude towards teaching profession is effective in predicting expected possible selves, but it is ineffective in predicting feared possible selves that are feared. Suggestions were made in line with the results obtained from the research.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".