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
COVID-19 restrictions have transitioned in-person qualitative research interviews to virtual platforms. The purpose of the current article is to detail some benefits and concessions derived from our experiences of using Zoom to interview men about their intimate partner relationship breakdowns and service providers who work with men to build better relationships. Three benefits; 1) Rich therapeutic value, 2) There’s no place like home, and 3) Reduced costs to extend recruitment reach and inclusivity, highlighted Zoom’s salutary value, the data richness afforded by being interviewed from home, and the potential for cost-effectively progressing qualitative study designs. In particular, reduced labour and travel costs made viable wider reaching participant recruitment and multi-site data collection. The concessions; 1) Being there differently, 2) Choppy purviews and 3) Preparing and pacing, and adjusting to the self-stream revealed the need for interviewers to nimbly adjust to circumstances outside their direct control. Included were inherent challenges for adapting to diverse interviewee locations, technology limits and discordant audio-visual feeds. Amongst these concessions there was resignation that many in-person interview nuances were lost amid the virtual platform demanding unique interviewer skills to compensate some of those changes. Zoom interviews will undoubtedly continue post COVID-19 and attention should be paid to emergent ethical and operational issues.
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 | 0.023 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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