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Solid‐waste contracting‐out, competition, and bidding practices among Canadian local governments

2001· article· en· W1981480714 on OpenAlexaffabout
James C. McDavid

Bibliographic record

VenueCanadian Public Administration · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBiddingCompetition (biology)BusinessUnit (ring theory)Service (business)PopulationFinancePublic serviceIndustrial organizationEconomicsPublic economicsMarketingPublic administration

Abstract

fetched live from OpenAlex

Abstract: Canadian local governments continue to rely on private contractors to produce services for their residents. A key expectation for those who contract out services is that unit costs will be lower than the costs incurred if public crews and equipment produced the same service. A principal reason for expecting lower costs is the assumption that private contractors are exposed to competition that induces companies to operate with mixes of capital, labour and technologies that are more efficient. This article compares public and contracted private production of residential solid‐waste collection in 327 local governments across Canada. Three complementary hypotheses that are grounded in the theory and research on local public economies are tested. The findings generally support contracting‐out as a way to reduce unit costs, although substantial public‐private differences occur only in communities less than 10,000 population. Further, where communities have divided up their residential solid‐waste collection between public and private producers, overall costs are lower than national averages, and contracted companies are substantially less costly than their public counterparts in the same local governments. Finally, in communities that have contracted out this service, the competitiveness of the bidding practices affects unit costs. Local governments that bid the service competitively enjoy a cost saving compared to those that renew their contract with the existing company.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.449

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.002
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.043
GPT teacher head0.255
Teacher spread0.211 · 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 designObservational
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

Citations37
Published2001
Admission routes2
Has abstractyes

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