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Record W7071029073

Rising Disability Payments: Are Cuts to Workers' Compensation Part of the Story?

2015· other· en· W7071029073 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2015
Typeother
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Social securityDisability insuranceCompensation (psychology)WorkforceWorkers' compensationDisability benefitsState (computer science)Entitlement (fair division)
DOInot available

Abstract

fetched live from OpenAlex

There has been a large increase in the number of workers receiving Social Security Disability Insurance (DI) over the last quarter century. While most of this increase is explained by well-known demographic factors, such as the growing number of women in the workforce and the aging of the baby boomers, there is considerable concern that workers are increasingly choosing to collect DI benefits as an alternative to working. This concern has figured prominently in the debate over plans to maintain full funding for the DI program beyond the projected DI trust fund depletion date in late 2016.This paper examines the extent to which cuts in state workers' compensation (WC) benefits may have contributed to the rise in DI awards. To some extent, these programs may be seen as alternative sources of support for workers with job-related injuries. Insofar as injured workers are less able to receive WC benefits, they may be more likely to turn to the DI program. At the national level, there is a clear correlation between the sharp decline in WC benefits over the last quarter century and the rise in DI benefits. This paper examines whether there could be a causal relationship between the reduction in WC benefits and the rise in DI benefits by examining state-level data.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.130
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.262
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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