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Record W1607838339

The Road to Transformation: Ascending from the Decade of Darkness

2007· article· en· W1607838339 on OpenAlexaboutno aff
Bernd Horn, Bill Bentley

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMilitary, Security, and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTransformation (genetics)DarknessComputer scienceBotanyBiology
DOInot available

Abstract

fetched live from OpenAlex

Nobody likes mistakes. Fewer yet like to revisit errors—to analyze, discuss or study them. They are often an embarrassment and remind us of our fallibility and shortcomings. It is always much easier to celebrate our achievements and successes—that leaves everyone with a warm feeling. However, although it is always preferable to avoid making mistakes, once they occur they are important and must be recognized as such. They speak to our weaknesses as both individuals and institutions. They are signals, if not alarms, to warn us of deficiencies that must be addressed. In fact, it has often been said for good reason that one can learn more from one’s mistakes than from one’s successes.\nThe military has always been bad at accepting this premise. Mistakes are often construed as a sign of weakness or inability and many perceive them as potential career-ending events. Such a zero tolerance to mistakes breeds an environment of risk aversion, micro-management and stagnation. It kills initiative and experimentation. And, it avoids examining mistakes in detail—lest blame insidiously spread its evil tentacles and taint others in the chain of command. However, this state affairs leads to atrophy within an organization.\nIt takes strong will and determination to break such a cycle. Normally, crisis is the only catalyst that compels leadership within an organization to take action, and even then it is difficult. The Department of National Defence (DND) and the Canadian Forces (CF), particularly the officer corps, found themselves in such a situation in the late 1980s and 1990s. By 1997, they were at the lowest ebb of their history. They had lost the confidence and trust of the government and Canadian people they served. They were stripped of their ability to investigate themselves. Furthermore, they were not trusted to implement the recommended changes forced upon them by the government and an external committee was established as a watchdog. Whether the leadership wanted to admit it or not, and they vehemently denied it at the time, there existed some substantial and deep rooted problems with DND, the CF and the officer corps. They were caught in a decade of darkness.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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
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

Citations2
Published2007
Admission routes1
Has abstractyes

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