Where is Harry Hopkins Hiding? Recovering Insights from the New Deal for Management History
Bibliographic record
Abstract
Despite calls for more work to reveal processes of neglect in historicizing the individual, the field has only now begun surfacing key junctures and hidden figures from the past with insights and contributions to management. The New Deal and its untold influence on shaping the intellectual heritage of management and organization studies is one of many examples of this historical-narrative privileging. In this paper we revisit the potential of the New Deal as a research context and attempt to bring forward one of its chief architects, Harry Hopkins. Using elements from ANTi-History and microhistory, we follow the life and work of Hopkins to surface the role he played in the New Deal, his influence in the Roosevelt Administration, and his contribution to early management and organizational thought vis-à-vis crisis management. Hopkins, his politics, and lessons from the New Deal – how each prove relevant for MOS today – is discussed. The paper concludes with a call for space dedicated to studying topics and individuals neglected, marginalized, or forgotten in history.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".