Bibliographic record
Abstract
Archive of Forgetfulness is a catalogue of the pan-African digital exhibition and podcast series which ran from September 2020 and December 2021 at archiveofforgetfulness.com. The publication acts as a physical translation of the collection of work online, and opens up wider questions around archives, memory and forgetfulness. The project includes the work of fifty-six artists, cultural producers, curators, creative thinkers and researchers from the African continent and diaspora, including Angola, Brazil, Canada, Democratic Republic of Congo, Egypt, France, Ghana, Kenya, Libya, Mauritania, Morocco, Mozambique Nigeria, Rwanda, South Africa, Sudan, Tanzania, Uganda, USA, the United Kingdom and Zanzibar. The catalogue speaks to the four parts of this larger project, namely an eight-part podcast series, twenty-two art works submitted in response to an open call, five essays and six regionally curated projects. As a collection of work centred on the African continent, the various contributors interrogate archival gestures, raise questions on personal and political histories that emerge via infrastructures of mobility, and suggest ways of living and remembering for alternative possible futures. In these works, archival labour and memory work are understood as deeply political, personal and speculative.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".