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Record W7065754002

Fatal Leadership Approaches

2017· other· en· W7065754002 on OpenAlexaboutno aff

Bibliographic record

VenueUTS ePRESS (University of Technology Sydney) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCommitSuicide preventionLeadership styleAuthoritarianismRelation (database)Poison controlWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

Work related suicide is rarely studied.Professor Stewart Clegg -one of our most published and cited researchers on strategy and organizations -is one of the few, who has recently researched the matter.I catch him on his way through Lyon, France, to get his thoughts on fatal leadership approaches and an authoritarian leadership style in particular. Fatal Leadership Approaches -Can Leadership Kill?Can bad leadership kill?To most of us the question sounds extreme.The answer is: yes, bad leadership in our workplaces can kill the will to live.It is one of those topics largely left in the dark.We don't talk so much about it, we don't research it much and it is rarely part of any public debate.Every year, 800,000 people commit suicide.The World Health Organization estimates that for each death, there are about 20 attempted suicides.Therein each is estimated to have on average three or four family members, this equals the entire populations of Canada and Australia combined being affected by suicide or attempted suicide every year.Moreover, suicide is in fact the second leading cause of death among 15-29 year olds globally (WHO).In relation to work, Suicide Prevention Australia estimates that about 17% of suicides in Australia are work-related.Furthermore,

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.109
GPT teacher head0.226
Teacher spread0.117 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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