Reflections on methodological reconsiderations and ethical procedures of a postdoctoral researcher
Bibliographic record
Abstract
This chapter contains an autoethnographic analysis of my postdoctoral experiences to describe and explain the fluid processes needed to see my project through to completion. Lockdown restrictions forced my proposed participants, Bachelor of Education final year students, to work online. Alongside this, the university’s ethics committee was unable to grant consent for a researcher to visit schools to interview teachers. These events led to modifications to the participant sample, the proposed recruitment process, and the research tool. There was a sense of starting again in terms of researching the role of social media for recruitment and developing an online survey for data collection. Advertising a research study to an unknown and vast cohort of teachers was impossible and I asked friends and colleagues to share my recruitment poster. It was pleasing that 13 teachers from Canada and England participated, the online survey responses were substantiated by five follow up interviews.
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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 | 0.481 | 0.431 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.069 |
| Scholarly communication | 0.027 | 0.022 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.016 | 0.054 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".