Bibliographic record
Abstract
The following paper is centered around the potential for organizational change in response to the COVID-19 pandemic. This paper argues that the disruption of “business as usual” during the COVID-19 pandemic provides opportunities to both highlight gendered organizational practices during remote work and explore how organizational actors might contribute to a more equitable restructuring of gendered communication practices once employees return to in-person work. First, the paper contextualizes the COVID-19 pandemic at the time of writing. Next, the literature review examines the notion of organizations as inherently gendered, the history of organizational change from Lewinian Planned Change to models of non-linear change, and bureaucratic organizational structures using a feminist lens. The discussion section then argues that complexity theories offer significant opportunities for improvement due to the destabilization of current workplace practices. This argument is followed up by examples of how organizations can successfully engage complexity theories to reduce gender inequality in the post-pandemic world. The paper concludes that by emphasizing consensus and autonomy, improvements to network communication and the merging of public and private spheres should be the first steps towards the ultimate goal of reducing gender inequality through the deconstruction of bureaucracies.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.033 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".