One within Many, Many within One
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
Teaching is one of the most consequential responsibilities of an academic librarian, yet many of us approach it without the training or self-awareness required to do it well. Teaching well means being willing to commit to endless, fearless exploration of pedagogical pathways, shifting social realities, and discomforting valleys within the self. These journeys enable us to define and strengthen our teacher identities. Critical LIS studies on identity frequently explore the multiplicity of librarian attitudes toward teaching or the complexity of individual librarian identities. In our study, we merged these two exploratory objectives by analyzing the dialogical interaction of an academic librarian's multiple identities in the teaching context, specifically. As academic librarians, diverse in terms of race, gender, age, and professional experience, we engaged in collaborative autoethnography to uncover and name the interlocking identities that inform our teaching endeavours. Through the lens of dialogical self theory (DST) and its concept of self positioning, we identified positions of the self that interact and negotiate with each other to facilitate or complicate the act of teaching itself. Autoethnographic exploration deepened our understanding of our teaching selves and helped us decipher the socio-psychological scripts that hinder and empower us as educators.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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