Archival Inversions: Rethinking Knowledge Infrastructures through the CUNY Distance Learning Archive
Bibliographic record
Abstract
In the spring of 2020, doctoral students at the City University of New York (CUNY) Graduate Center produced a collective intervention in the knowledge infrastructures of the largest public university system in the United States. The CUNY Distance Learning Archive (CDLA) sought to document and bridge the lived experiences of faculty, staff, and students—particularly immigrant and working-class undergraduates—across the 25 campuses comprising the CUNY system. The archive’s three public-facing collections span the closure of the university, the transition to remote teaching and learning, and the resulting activism of the #CutCOVIDNotCUNY movement pushing against austerity in public higher education. These collections therefore highlight the various tension spaces made visible by the breakdown and maintenance of university infrastructure during this time of crisis. Here, the CDLA builds on the concept of infrastructural inversion by enacting a kind of “archival inversion” that inscribes these fissures into our institutional memory as a public university. By putting in conversation otherwise disjunctured constituencies and discourses, the collections in the archive offer an opportunity to rethink the relational nature of infrastructure and the frozen dialectics that are made visible by moments of crisis. This article takes the infrastructural interventions of the CDLA collections as a starting point to explore questions surrounding digital archival approaches to times of crisis and how these endeavours might facilitate prefigurative politics and memory-making practices.Au printemps 2020, des doctorants du Graduate Center de l'université de la ville de New York (CUNY) sont intervenus au niveau collectif sur les infrastructures de connaissances du plus grand système universitaire public des États-Unis. Les Archives Universitaire de l'Enseignement à Distance (CDLA) ont cherché à documenter et à relier les expériences vécues par le corps enseignant, le personnel et les étudiants - en particulier les immigrés et les étudiants de la classe ouvrière - sur les 25 campus du système de la CUNY. Les trois collections publiques des archives couvrent la fermeture de l'université, la transition vers l'enseignement et l'apprentissage à distance, ainsi que l'activisme résultant du mouvement #CutCOVIDNotCUNY qui pousse contre l'austérité dans l'enseignement supérieur public. Ces collections mettent donc en lumière les différents espaces de tension rendus visibles par la rupture et le maintien des infrastructures universitaires pendant cette période critique. Ici, le CDLA s'appuie sur le concept d'inversion infrastructurelle en mettant en œuvre une sorte d'"inversion archivistique" qui inscrit ces fissures dans notre mémoire institutionnelle en tant qu'université publique. En mettant en conversation des groupes et des discours autrement disjoints, les collections des archives offrent l'opportunité de repenser la nature relationnelle de l'infrastructure et les dialectiques figées qui sont rendues visibles par les moments de crise. Cet article prend les interventions infrastructurelles des collections du CDLA comme point de départ pour explorer les questions relatives aux approches des archives numériques en temps de crise et la manière dont ces efforts peuvent faciliter les politiques préfiguratives et les pratiques mémorielles.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.031 | 0.037 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.003 | 0.031 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".