One Loveheart at a Time: The Language of Emoji and the Building of Affective Community in the Digital Medieval Studies Environment
Bibliographic record
Abstract
The Old Books, New Science (OBNS) Lab began using Slack in May 2016 to facilitate the work of a diverse research group at the University of Toronto. Yet the OBNS Slack does not simply facilitate scholarly communication: it also serves as a powerful affective network, bringing together scholars in new and sometimes unexpected configurations. The affective language of emoji is fundamental to the growth of this community. Lab members coin new emoji that are taken up by the community eagerly, many of which are meaningful only within the OBNS environment. It is common to reference Slack emoji in in-person conversation; equally, the OBNS Slack is often home to advising sessions or meetings that in another workplace would take place face-to-face. In this way, the online environment of Slack and the in-person environment of the lab are mutually constitutive. Such usage of Slack may, however, also have a dark side: by celebrating affective community in the workspace, what happens to the distinction between home and office, and consequent erosion of leisure time? We consider whether the affective practices of the OBNS Slack might allow personal and professional boundaries to be blurred in such a way as to prioritize the personal.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".