Bibliographic record
Abstract
Purpose The purpose of this paper is to understand what are the best projections of these events effects on organizations and economies. The onset of the COVID-19 pandemic leads to a combination of economic and public health circumstances that challenge the accounting for and accountability of organizations that are mostly outside of their experience and that of academics for the past 50 years. Design/methodology/approach Through evidence-based policymaking research, evaluation and reporting tools the author draws on the extant research literature to develop estimates of likely effects of these events on organizations and economies. Findings The process of investigating this subject led the author to write a short research synthesis paper (Salterio 2020a) that summarized the historical economic evidence about the Spanish flu of 1918–1920 and various simulations of potential pandemic macroeconomic effects. This evidence allowed the author to quantify the potential effects of the crisis less than a month into the North American economic shutdown. Originality/value Using that research synthesis the author responded to the call for papers for this special issue by reflecting on the lessons that this crisis has for managers and organizations from both an accountability and accounting perspective.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".