Bibliographic record
Abstract
Following my archival impulse, I access memories in the form of 8mm footage shot in 2002 - 2004 by my family members to read my own history. Taking an anti-masterful approach and building upon and working with the media to better understand what constitutes my being here as a particular kind of subject, I simultaneously meditate on the notion of memory, access, sound and the state of the post-digital. The installation RAM (Random-Access Memory) consists of a set of digitally recreated images, aligned to their original 8mm footage projected on the opposite wall. The original footage playing on a loop, is displayed next to a collection of soundtracks inspired by the memories from my childhood and the footage shot by my uncle across India and Italy. When we think of ourselves outside of the Grid of 9-5, which physical form and form of thought do we assume then? To what extent are our everyday experiences shaped by the past and to what extent is the past still happening? \nIn this installation I extend a hand from somewhere within this bundle of knots, outward and inward, relentlessly trying to trace my formative narratives that in ways both clear and less accessible, have shaped my self-understanding as a particular kind of subject in this country and economy.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.009 |
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".