Encountering Berlant part one: Concepts otherwise
Bibliographic record
Abstract
Abstract In Part 1 of ‘Encountering Berlant’, we encounter the promise and provocation of Lauren Berlant's work. In 1000‐word contributions, geographers and others stay with what Berlant's thought offers contemporary human geography. They amplify an encounter with their work, demonstrating how a concept, idea, or style disrupts something, opens up a new possibility, or simply invites thinking otherwise. The encounters range across the incredible body of work Berlant left us with, from the ‘national sentimentality’ trilogy through to recent work on negativity. Varying in form and tone, the encounters exemplify and enact the inexhaustible plenitude of Berlant's thought: fantasy, the case, love, impasse, feel tanks, slow death, ellipses, gesture, attrition, intimate public, ambivalence, style. Part 2 of ‘Encountering Berlant’ focuses on Berlant's most influential concept: ‘cruel optimism’. Across these heterogeneous encounters, Berlant's enduring concern with the tensions and possibilities of relationality and how to enact better forms of common life shine through. These enduring concerns and Berlant's commitment to the incoherence and overdetermination of phenomena are summarised in the Introduction, which also explores how Berlant's work has been engaged with in geography. The result is a repository of what an encounter with Berlant's thought makes possible.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.059 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".