Negotiating informed consent: A students-as-partners perspective
Bibliographic record
Abstract
Building on our 2019 ISSOTL poster presentation, we (Michael: a tenured English Department faculty member; and Emily: an undergraduate English major) are developing a reflective essay about our in-progress SaP project designed to assess the effectiveness of recent university system mandated curricular changes to multiple sections of an introductory college-level writing course at our home institution, the University of North Georgia, USA. Even though we received the necessary institutional and federal government permissions to conduct this research, and even though the research participants signed the necessary informed consent document, as we continue our data collection we wonder how we might better listen and watch for what Bivens (2018) calls “microwithdrawals of consent.” Bivens describes this phenomenon as the “implied or partial halt of a person’s willingness to participate in one or more aspects of the research process and the researcher’s awareness of that withdrawal.” Bivens calls on researchers to stay attuned to the participant’s body language and vocal tone to notice when participants may want to withdraw consent but not explicitly say so. When undergraduate researchers conduct interviews with other undergraduates, they are well-positioned to perceive these microwithdrawals of consent. With Emily as the lead author for this proposed piece, we wonder: How can students working in partnership with faculty help faculty better understand how informed consent is an on-going and negotiated process that does not end when research participants sign a consent document? Pondering this question emphasizes the “messy, ‘work in progress’ nature of SaP” (Matthews, 2017, p. 4), which, we argue should hold a central place in our SaP publications.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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 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".