Ethical and Friendly Researchers, but not Insiders: A Response to Blodgett, Boyer, and Turk
Bibliographic record
Abstract
This commentary is a response to the article by Lisa J. BLODGETT, Wanda BOYER, and Emily TURK (2005) in this issue of FQS. The original article describes ethical challenges and relational issues within a large, ongoing, qualitative study about the development of self-regulation in early childhood. Those authors focus in particular upon: (a) obtaining free and informed consent, (b) working with vulnerable populations, and (c) balancing insider and outsider roles. I identify some key strengths of the research that may provide useful models for other researchers, while cautioning against the evident overgeneralization of the term "insider." BLODGETT et al. clearly demonstrate that they are ethical and friendly researchers, but they are not insiders in the daycare settings where their research takes place. I conclude with a call for researchers to seriously consider and empirically document what it might mean to adopt a subject-centered perspective on research ethics. URN: urn:nbn:de:0114-fqs0503375
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 | 0.064 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.046 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.062 | 0.104 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".