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Record W6928887863 · doi:10.4224/40003508

The National Research Council of Canada's 2023-2027 departmental sustainable development strategy

2023· report· en· W6928887863 on OpenAlexaboutno aff

Bibliographic record

VenueNRC Digital Repository · 2023
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFractal and DNA sequence analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentSustainabilityTransparency (behavior)Government (linguistics)AccountabilityIndigenous

Abstract

fetched live from OpenAlex

The National Research Council of Canada's (NRC) 2023 to 2027 Departmental Sustainable Development Strategy (DSDS) highlights the ways in which the department will contribute to the Government of Canada's Federal Sustainable Development Strategy (FSDS), including specific actions that will be taken to address all 3 dimensions of sustainable development— social, economic and environmental— as well as indicators and targets that will be used to measure success. This is the NRC's second DSDS; its first, developed in 2019, covered the period of 2020 to 2023 and included 26 commitments to support sustainable development. This DSDS includes 59 sustainability commitments to be achieved over the next 4 years, each with its own performance metrics and targets. The DSDS reflects the NRC's key sustainable development priorities, including accelerating the development of clean, renewable fuels, and energy storage materials; developing the technologies needed to grow supply chains for low‑carbon mobility and sustainable transportation; supporting the Canadian aviation and construction sector's decarbonization transition; investing in and supporting the growth of Canada's clean tech sector; increasing the value of plant-based proteins and their co-products, supporting strong and resilient Northern and Indigenous communities; improving the delivery of secure, affordable and high-speed internet services in rural and remote communities across Canada through new technologies; fostering an inclusive and diverse workplace; and, transforming NRC operations to ensure they are sustainable and climate resilient. Transparency and accountability are central to the FSDS and corresponding departmental strategies; therefore, progress made against the NRC's DSDS commitments will be captured in 2 reports to be published in fall 2024-25 and 2025-26. These reports will outline key sustainability success stories, achievements to-date and areas where concerted effort will continue to be directed.

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)0.014
metaresearch head score (Gemma)0.024
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 score0.900
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.008
Science and technology studies0.0070.003
Scholarly communication0.0110.003
Open science0.0060.005
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0340.016

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 head0.099
GPT teacher head0.330
Teacher spread0.230 · 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
Published2023
Admission routes1
Has abstractyes

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