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Flexible Labor Markets

2017· other· en· W3044076778 on OpenAlexaff
Tara Vinodrai

Bibliographic record

VenueInternational Encyclopedia of Geography · 2017
Typeother
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCasualFlexibility (engineering)Labour economicsTemporary workBusinessSplit labor market theoryWork (physics)Secondary labor marketLabor relationsEconomicsIndustrial organizationMarket economyEngineeringPolitical science

Abstract

fetched live from OpenAlex

As the structure of the economy in advanced economies has shifted from one primarily based on manufacturing to one based on services, the labor market has also shifted toward greater levels of flexibility. This flexibility is expressed in a number of ways. First, labor market flexibility is embedded in government policy, and employment regulation and standards that govern how labor markets operate. Second, organizational practices such as project‐based work, teamwork, teleworking, homework, and nonstandard scheduling have become more widespread. Third, there has been an increase in nonstandard work, including self‐employment and part‐time, casual, contract, and temporary labor. At the firm level, this is viewed as advantageous as it allows firms to respond more easily to changing market conditions, as well as reducing their overhead costs. While there may be some benefits to individuals through the use of these various arrangements, many forms of flexibility in the labor market impose greater risks, as these forms of work are often precarious and uncertain. These risks are often felt unevenly across geographic space, industrial sectors, and gender, ethnicity, class, and immigration/citizenship statuses.

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 categoriesInsufficient payload (model declined to judge)
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 score0.158
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.020
GPT teacher head0.370
Teacher spread0.350 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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