MétaCan
Menu
Back to cohort
Record W4401809648 · doi:10.55016/ojs/sppp.v8i1.42551

2015 Status Report on Major Defence Equipment Procurements

2015· article· en· W4401809648 on OpenAlexafffundabout
David Perry

Bibliographic record

VenueThe School of Public Policy Publications · 2015
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsGlobal Affairs Canada
FundersMinistère de la Défense NationalePublic Works and Government Services CanadaCanadian Armed Forces
KeywordsProcurementBusinessMarketing

Abstract

fetched live from OpenAlex

Federal elections may be good for democracy, but the campaigns — particularly the lengthy one recently held in Canada — can be crippling for plans to better arm our military. Just before the election was called, there were public signs of important progress being made in what has long been a frustratingly slow and bureaucratically complex procurement process. But then the campaign left the Department of National Defence and other federal departments unable to secure approvals from either a defence minister or the Treasury Board, until the election ended and the new prime minister appointed the current cabinet. There had already been upheaval prior to that: In the first seven months of 2015, the three senior leaders at the Canadian Forces and the Defence Department (including the minister) had been replaced, along with many other people critical to the procurement process. In addition, there had been changes in the Public Works Department and the Defence Procurement Strategy Secretariat. Frustrating and disappointing delays have long been a matter of course in Canada’s defence procurement process. In 2014/15, the number of ministerial or Treasury Board approvals to allow projects to proceed was half of that in 2009/10. Yet the demand for approvals has not abated. In addition to the turnover of key figures involved in the procurement and approval process, delays have come from a number of major steps added to the process, making an already lengthy and complex system even more so. To be sure, these steps were added in the pursuit of improved financial management and project management, with the aim of addressing longstanding problems. But it will take years to see if those objectives have been realized. An irony here is that the budget for military procurement has increased. Between 2004 and 2009, the Defence Department’s procurement budget nearly doubled. But the funding was never matched by the capacity to manage it. In 2003, the Material Group had a ratio of 2,600 staff for every $1 billion in procurement funds. By 2009, the ratio had become 1,800 staff for every $1 billion in procurement funds. Since then, the ratio has only gotten substantially worse. New systems now require extensive analysis to determine if a more intensive Treasury Board review is required. A recently created panel designed to provide a “third-party challenge function” on requirements for major procurements has created some confusion among officials as to what documentation they should be producing to support procurement initiatives. And the panel’s terms of reference are extensive, ranging from evaluating a project’s alignment with government policy and the level of its fit with allies’ capabilities, to the role of Canadian suppliers and the anticipated support concept. Still, there are some indications that changes enacted in 2014 to the procurement process may eventually help mitigate delays in the future. There are continual improvements being made to the way the Defence Department conducts project costing as well as how the Treasury Board Secretariat evaluates the costs, which will help improve the compatibility between estimates and newly introduced frameworks. New methods of better prioritizing projects have also been introduced. And there are plans underway intended to reduce the time involved in the department’s internal approval processes. For now, however, these attempts at improvement have been focused on the lower-dollar-figure approvals done by the minister. It remains to be seen if, first, they work, and secondly, if they can then be used to facilitate Treasury Board approvals, as well.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.205
Threshold uncertainty score0.687

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.005
Science and technology studies0.0020.000
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2050.126

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 head0.056
GPT teacher head0.330
Teacher spread0.274 · 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

Citations1
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueThe School of Public Policy PublicationsSame topicTechnology Assessment and ManagementFrench-language works237,207