Building Bridges: A Conversation between Friends, about Language, Laziness, and Long-distance Running
Bibliographic record
Abstract
This conversation took place in a shared Google Doc over several occasions in April and early May 2021, between friends and colleagues, artists and writers, Hannah Clarkson and Matilda Tucker, in the context of an ongoing experiment in collaborative writing. In their individual and collective practices, Clarkson and Tucker explore potential embodiments in language(s) of thinking and dwelling in the ‘here and elsewhere’ of places and spaces they may not physically be in, across cultural, geographical and/or emotional distance. They are interested in how language can be employed as a tool for empathy beyond concrete linguistic understanding; how translation as method opens up to modalities of fictioning and collective storytelling; and writing as an experiment in sharing everyday struggles and building collective narratives of care. An attempt to bridge gaps between the here and elsewhere of Stockholm, Berlin and all the other places that in this time of pandemic we cannot be, the text below is not a conclusion but a conversation. It is a thinking out loud - or rather, on screen - together, on themes of language and translation; belonging and resisting; work and laziness; former and formless selves.
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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.032 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".