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Record W2184063420

Impact and Benefit Agreements: Are they working?

2010· article· en· W2184063420 on OpenAlexaboutno aff
Jason Prno, Ben Bradshaw, Dianne Lapierre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Resource (disambiguation)Public relationsBusinessFocus groupCorporate governancePolitical scienceEnvironmental resource managementEnvironmental planningGeographyMarketingEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

The emergence of Impact and Benefit Agreements (IBAs) in the Canadian mining sector has been read by many as a positive governance innovation. Negotiated directly between mineral resource developers and Aboriginal communities with limited government interference, IBAs serve to manage impacts associated with a mining project and deliver tangible benefits to local communities. Notwithstanding their increasing use and significance, limited systematic analysis has been undertaken to determine whether they are, in fact, working. This paper reports on the effectiveness of a number of IBAs negotiated in support of three northern Canadian diamond mines, drawing on evidence from time-series data, key informant interviews, and focus group meetings in Yellowknife and Dettah, NWT, and Kugluktuk, NU. While some deficiencies were apparent and perceptions of effectiveness varied somewhat by Aboriginal community, the IBAs were generally found to be meeting their objectives, especially with respect to the delivery of benefits. For Aboriginal communities affected by mineral development in the Canadian North, this represents a significant change to typical outcomes of the past. Moving forward, research on IBA effectiveness needs to adopt a longer timeframe and begin to gauge the degree to which IBAs are able to address long-standing concerns associated with hinterland resource extraction beyond their agreement-specific objectives.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.340
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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.

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

Citations14
Published2010
Admission routes1
Has abstractyes

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