The City Under COVID‐19: Podcasting As Digital Methodology
Bibliographic record
Abstract
This critical commentary reflects on a rapidly mobilised international podcast project, in which 25 urban scholars from around the world provided audio recordings about their cities during COVID-19. New digital tools are increasing the speeds, formats and breadth of the research and communication mediums available to researchers. Voice recorders on mobile phones and digital audio editing on laptops allows researchers to collaborate in new ways, and this podcast project pushed at the boundaries of what a research method and community might be. Many of those who provided short audio 'reports from the field' recorded on their mobile phones were struggling to make sense of their experience in their city during COVID-19. The substantive sections of this commentary discuss the digital methodology opportunities that podcasting affords geographical scholarship. In this case the methodology includes the curated production of the podcast and critical reflection on the podcast process through collaborative writing. Then putting this methodology into action some limited reflections on cities under COVID-19 lockdown and social distancing initiatives around the world are provided to demonstrate the utility and limitations of this method.
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 | 0.044 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.027 | 0.058 |
| Scholarly communication | 0.038 | 0.015 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.015 |
| 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".