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Record W235508859

Operation Assistance: Canadian Civil Power Operations

2001· article· en· W235508859 on OpenAlexaboutno aff
W. Semianiw

Bibliographic record

VenueMilitary review · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsInfantryJurisdictionAgency (philosophy)Public administrationGovernment (linguistics)Unit (ring theory)Flood mythLocal governmentPolitical scienceLawSociologyGeographyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

INSIGHTS Canada's military forces have a long history of coming to the aid of civil powers during national emergencies. In every instance, military forces cooperated closely with civil authorities to accomplish necessary tasks. This was also the case during Operation Assistance, when Canadian Forces (CF) gave support to the Manitoba government during the of 1997-Canada's flood of the century. The First Battalion Princess Patricia's Canadian Light Infantry of Calgary, Alberta, deployed to an area of approximately 500 square kilometers south of Winnipeg, Manitoba, north of Grand Forks, North Dakota. The area included five regional municipalities (RMs) each with its own elected rural official (Reeve). Each RM, under the direction of its Reeve, was the lead agency local operations. All CF units were to support and assist the RMs. The operation provided many lessons learned from aiding civil powers during a natural disaster. Players and Boundaries At the tactical level, the players during the crisis included varied groups of government and nongovernment, civilian, and commercial interests. In Manitoba this included the Reeve, his public administrator, the local fire department, the Royal Canadian Mounted Police (provincial jurisdiction), the Ministry of Natural Resources, Manitoba Hydro, Manitoba Telephone, Manitoba Highways, Manitoba Emergency Measures Organization (EMO), and the mayors and councils of affected towns. That lines of operation crossed municipal, provincial, and federal jurisdictions quickly became evident. Each agency had its own area of responsibility and coverage, but areas often overlapped, thus increasing the strain on coordination. However, agencies that aligned along municipal boundaries or congruence with the lead agency found their support efforts simplified and streamlined. The tasks the military were to perform centered on general duties such as filling sandbags, building dikes, or performing rescue, traffic control, or escort duties. Because large urban and county areas would be uninhabited, providing armed security was, at first, viewed as a probable task. However, this did not prove to be necessary. Sufficient police resources were available and were deployed effectively to permit or deny access to controlled areas. To achieve the tasks expected of them, the military organization of company, squadron, and battery, with their inherent mobility, communications, and general-purpose soldiers, proved to be best suited for the tasks that were to be conducted. Military Force Organization During the staff planning process, planners arrived at two options for the organization of military forces support of civil authorities. Military forces could take a centralized approach which the unit would control and allocate resources to civil authorities based on the task, or they could take a decentralized approach which each RM would be assigned a slice of the pie. Situation analysis revealed that a decentralized approach would be best because it best fulfilled the need for simplicity, time, and space; unity of effort; and unity of command and control. Rifle companies in support. Rifle companies were allocated support to RMs. Major towns, where dikes had been built before the flood, received as a military point of contact, a liaison officer (LO), who was generally a senior noncommissioned officer. Twining a rifle company with an RM and placing an LO each town proved to be effective. Civil authorities each RM and the towns preferred to work with the same commander for all aspects of the operation. Local officials and military commanders developed relationships and dependencies that proved beneficial. Also, military commanders became versed the nature of the crisis and the needs associated with their RMs and towns. Each RM and town had its own way of fighting the and supporting its residents. …

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)0.001
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.137
Threshold uncertainty score0.993

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.010
Science and technology studies0.0100.002
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0410.003

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 head0.018
GPT teacher head0.267
Teacher spread0.250 · 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
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
Published2001
Admission routes1
Has abstractyes

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