Bibliographic record
Abstract
As cultural work becomes increasingly precarious and digitally mediatized, we ask how mentorship can continue to play a crucial role in shaping how the know-how of cultural work is organized and shared. Drawing on a case study of a mentorship program for cultural work taking place in Greater Vancouver, Canada, we propose conceptualizing mentorship as a figuration, which we describe by tracing three interdependent threads: a constellation of actors, frames of relevance, and communicative practice. Focusing on the constellation of actors, we analyse how the mentorship spans across a dynamic network of actors whose connected presence shape the sharing of the know-how and distributed knowledge among heterogeneous mentors and mentees. Turning to the frames of relevance, we find that mentorship is much more than a means of skill transmission. Instead, it is a site where struggles over the meaning of know-how for cultural work - frames of relevance including skill, creativity, and social justice - take place. Finally, by foregrounding the communicative practice for the figuration of this mentorship, we illustrate how mentorship is facilitated through various layers of digitally mediated exchange which reconfigure traditional notions of guidance and support for sharing experience and knowledge for cultural work. Reconceptualizing mentorship as a figuration allows us to move beyond accounts of dyadic relationships between an individual mentor and a mentee working in co-presence, and instead recognize the relational, institutional, and technological dynamics that structure contemporary cultural work. Ultimately, our findings suggest that, through the figuration of mentorship for cultural work, mentors and mentees can still share meaningful collective experiences and knowledge, which expands our understanding of the know-how necessary to practise cultural-work and how it is shared.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.025 | 0.075 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".