Bibliographic record
Abstract
The emergence of the Covid-19 pandemic brought many changes to daily life in Canada. One such behavior that surfaced was what could be defined as ‘panic hoarding,’ namely, the purchasing of items such as toilet paper, sanitizer and disinfectant in far greater quantities per person than other times, which risked the creation of shortages across communities. In order to understand such behavior, this article will use ideas from Émile Durkheim to analyze the relationship of social media and its impact on the behavior of panic hoarding. In particular, Durkheim’s concepts of collective consciousness show how social media provides enough impetus to make the case that this pandemic is better defined by mechanical than organic solidarity. We can see social media as the vehicle through which collective consciousness can be experienced, and more immediately so at this time, insofar as we see how it influences panic hoarding behavior. We can also see that social media’s use of memes can be likened to totems, and that they give clues to the values we hold at this time.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".