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Record W3015972992 · doi:10.1111/anti.12628

From the School Yard to the Conservation Area: Impact Investment across the Nature/Social Divide

2020· article· en· W3015972992 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAntipode · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsImpact investingEliteFinanceFace (sociological concept)Investment (military)EconomicsSociologyPolitical scienceSocial scienceEmerging marketsPolitics

Abstract

fetched live from OpenAlex

Abstract In the face of planetary crises, from inequality to biodiversity loss, “impact investing” has emerged as a vision for a new, “moral” financial system where investor dollars fund socio‐environmental repair while simultaneously generating financial returns. In support of this system elite actors have formed a consensus that financial investments can have beneficial, more‐than‐financial outcomes aimed at solving social and environmental crises. Yet critical geographers have largely studied “green” and “social” finance separately. We propose, instead, a holistic geography of impact investing that highlights the common methods used in attempts to offset destructive investments with purportedly reparative ones. This involves interrogating how elite‐led ideas of social and environmental progress are reflected in investments, as well as deconstructing the “objects” of impact investments. As examples, we use insights from both “green” and “social” literatures to analyse the social values embedded in projects of financialisation in schooling and affordable housing in the US.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.077
GPT teacher head0.300
Teacher spread0.222 · 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