Human depreciation accounting and the emergence of industrial pensions
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate how the accounting notion of “human depreciation” helped the defined benefit pension plan emerge as the dominant means of dealing with an aging workforce in the first half of twentieth century USA. Design/methodology/approach – The study examines historical material to identify the intersection of several different practices and knowledges that came together in the early decades of the 1900s to permit human depreciation in 1949 to be used to formally link aging employees to their employers. Findings – In the early part of the twentieth century, humans and machines were constructed as parts of a single productive system, human traits were studied in order to increase their machine-like capacities, in the hope of creating a more efficient industrial economy. At the same time, fatigue associated with this industrial nation was constructing the older worker as subject to decline, hence opening the door to a linkage to physical and economic depreciation. Social implications – Reveals that the language of accounting can be utilized by non-accountants and outside organizational boundaries to effect public policy and play a constitutive role. Thus, who is able to use accounting conventions is important in understanding how accounting shapes social settings. Originality/value – “Human depreciation”, used by the Steel Industry Board in 1949 to assign responsibility to employers for the depreciation of their human assets, has been left unexamined despite being cited as one of the greatest contributors to the growth of industrial pensions in the USA. The study examines accounting as an interface among and between the organizational and “non-accounting” spheres.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".