The Road to Transformation: Ascending from the Decade of Darkness
Bibliographic record
Abstract
Nobody likes mistakes. Fewer yet like to revisit errorsâto 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 successesâthat 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 oneâs mistakes than from oneâs 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 detailâlest 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".