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Record W2159993373

Sustainability reporting guidelines mapping and gap analyses for Shanghai stock exchange

2011· article· en· W2159993373 on OpenAlexaboutno aff
Syntao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessSustainability reportingAccountingGeneral partnershipCorporate governanceReputationStock exchangeSocial sustainabilitySustainability organizationsChinaCorporate social responsibilityPublic relationsFinancePolitical science
DOInot available

Abstract

fetched live from OpenAlex

IFC and the Shanghai Stock Exchange (SSE) worked with SynTao on a study of existing sustainability reporting guidelines internationally and in China, comparing these with the existing SSE requirements. The study included interviews with key stakeholders and a review of the characteristics of 25 existing international and national frameworks. It confirms that, though sustainability reporting in China has increased in recent years, there is still much work to do to improve the quality and usefulness of environment,social and governance (ESG) information provided to stakeholders, particularly investors. According to Klynveld Peat Marwick Goerdeler, in 2008 79 percent of 250 global companies published sustainability reports. In China the number of sustainability reports reached over 700 in 2010. There is a widely established expectation that companies wanting to obtain a leadership position and become competitive in the global marketplace need to effectively manage their environmental and social performance, disclosing challenges and achievements in a sustainability report. Moreover, corporate product and service innovation should aim to contribute to society's well-being. Studies show that in China, however, most companies release sustainability reports for reasons of reputation and development of government relationships, not fully taking advantage of opportunities for risk management and investor relations. The study included a wide literature review and interviews with a selection of key stakeholders. This report was published in partnership with Canada.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.013
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0150.017
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.374
GPT teacher head0.382
Teacher spread0.008 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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

Citations0
Published2011
Admission routes1
Has abstractyes

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