MétaCan
Menu
Back to cohort
Record W7023813765

The Practice of Place in Philanthropy: Grantmaking by Private Foundations in Canada and Australia

2022· other· en· W7023813765 on OpenAlexaboutno aff

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsTypologyFoundation (evidence)Private sectorVariety (cybernetics)NegotiationInterdependenceFocus (optics)Reciprocity (cultural anthropology)Element (criminal law)
DOInot available

Abstract

fetched live from OpenAlex

Effective models of place-based funding remain conceptually unresolved. Place-based, collective impact initiatives are increasingly recognised for creating long-term systems change, yet the role of philanthropy, specifically private foundations, in supporting, advocating for and catalysing change is underexplored (Husted et al., 2021). This paper provides insights into the internal factors influencing a private foundation’s approach to place-based work.<br/><br/>Interest in place as an animating concept is beginning to permeate research in philanthropy (Hopkins & Ferris, 2015; Never & Westberg, 2016; Pill, 2017; Williamson et al., 2021). However, we know little about place-based grantmaking by private foundations, which unlike community foundations are not inherently rooted in place. Where, and at what scale, do private foundations focus their grantmaking? With what purposes and what effects? The concept of being place-based as an effective model of grantmaking needs to be more fully analyzed, conceptually and empirically.<br/>This paper draws on the expanding literature on place-based analysis to conceptualize and assess grantmaking practices of private foundations, comparing Canada and Australia. We first describe a three-part typology of the concept of being ‘place-based,’ in contrast to being cause-oriented. 1) ‘Proximate’ involves support for organizations and causes familiar to private foundations by mere geography – giving where they live, without a strategy for social or systems change (Glückler & Ries, 2012). 2) A more intentional ‘strategy-based’ approach necessitates greater engagement and commitment over time, and involves selecting locales to benefit from foundation support. These may be close to foundation offices or more distant communities, . 3) A ‘systems-change x place’ approach entails investing in deeper knowledge about community and place, collaboration and committing for a longer term. We then ask: do private foundations concentrate on one, or a mix, of these place-based grantmaking approaches ? Is there an underlying strategy, and what are the implications for foundations and grantees?<br/><br/>Two key factors are hypothesized to explain more place-engaged strategic and systems change approaches: foundation leadership, and idea transfer and learning. Specifically, we argue that when boards and senior managers include younger and more diverse leaders, foundations are more likely to move beyond the proximate. We also anticipate a contagion or transfer effect whereby foundations emulate effective place-based models of peer foundations. Our empirical analysis examines grantmaking by 25 private foundations in each of Canada and Australia over the past decade. For Canada, data on the locations of grants are geocoded from annual charitable tax returns, which include the amount and recipient of every gift. For Australia, data are compiled from multiple public and organizational records. Content analysis of foundations’ and grantees’ websites and annual/evaluation reports is then used to assess the purpose and underlying strategy of these grants, and compare practices to the typology.<br/><br/>By delineating and comparing place-based grantmaking practices of private foundations in Canada and Australia, we provide empirical evidence of communities and funders working collectively to tackle entrenched and complex problems.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.234
Teacher spread0.226 · 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.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueQUT ePrints (Queensland University of Technology)French-language works237,207