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Current State of the Workforce: The Changing Psychology of the Workforce

2025· book-chapter· en· W4414710353 on OpenAlexaff
Edwin Mouriño-Ruiz

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsWorkforceHumanityEmpathyPosition (finance)Work (physics)Line managementState (computer science)

Abstract

fetched live from OpenAlex

Abstract This chapter looks at today’s workforce and the different expectations that they have from their organizations and leaders. They are looking for purpose (irrespective of age and length in the workplace), happiness, autonomy, psychological safety, respect, to be listened to. They are looking for empathy and leaders that appreciate what they do and healthy relationships with those they work with and their leaders. In summary, they are looking for leaders to care about them and unfortunately more of those in a management position do not know how. The organizations and leaders that provide these will have a competitive edge over others. The organizations that focus on the humanity in their workforce has and will see positive bottom line implications. For this to happen they will need human-centered organizational cultures that positively impacts their workforce. Last, this is not just me saying this. There is extensive research that has come out and continues to come out on this topic.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0070.007
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.017
GPT teacher head0.245
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2025
Admission routes1
Has abstractyes

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