Archive of the Digital Present (ADP), COVID-19 Period: Collecting and Visualizing Metadata of Online Literary Events Hosted in Canada, March 2020 - September 2021
Bibliographic record
Abstract
Archive of the Digital Present for Online Literary Performance in Canada (COVID-19 Pandemic Period) is a research and development project that arises out of the need to address foundational, practical and theoretical research questions about the impact of the recent (and ongoing) COVID-19 pandemic, and attendant social disruptions and restrictions, upon literary communities in Canada through the study of organised literary events as they have occurred online since March 2020. \n \nThe papers that constitute this panel focus on the design and development work pursued in building a searchable, open access database and directory – The Archive of the Digital Present (ADP) – to allow scholars, literary practitioners, and the public to gain knowledge about the nature and significance of literary events (online, hybrid, and in-person) that have occurred during the pandemic period, through the collection and structuring of metadata, and, in some cases, with direction to audiovisual (AV) documentation of the events themselves as they were held using platforms such as Zoom and YouTube. \n \nOur papers explain key facets of development by presenting approaches to (1) data collection and structuring, (2) stack development, (3) data visualisation, and (4) front end design, that have emerged through the process of community and user-oriented design research and development used to create the ADP.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".