Achieving Success Through Innovation - Building a Road, Building Benefits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When the East Side Road Authority (ESRA) was given the mandate from the Manitoba provincial government to construct an all-season road linking 13 First Nation communities, one of the key components of its mandate was to ensure that the construction of the road was carried out in a manner that provides benefits to the affected First Nation communities. Since 2009, ESRA has been working with each of the First Nation communities to create and increase benefits to the communities, through the use of Community Benefits Agreements. This paper will examine the unique Community Benefits Agreements created by ESRA for the East Side Road Project. The goal of the Community Benefits Agreements is to have First Nation-owned, COR certified construction companies in each of the 13 communities. ESRA works in partnership with each community to establish these companies and to develop and mentor the company employees in all facets of the company: construction, project management, safety and environmental management, asset management and financial management. ESRA negotiates untendered, multi-million dollar contracts with First Nation owned construction companies to provide pre-construction and construction services related to the all-season road project. With the experience gained from working with ESRA, companies are then in a position to bid competitively on the road construction contracts. By working in partnership with the First Nation communities, and with a mandate to provide and increase benefits, the ESRA Community Benefits Agreement model is achieving success on the multi-billion dollar East Side Road project. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it