MétaCan
Menu
Back to cohort
Record W7052961106

Three essays on the impact of tax incentives and determinants of charitable giving

2019· dissertation· en· W7052961106 on OpenAlexaboutno aff

Bibliographic record

VenueThinkTech (Texas Tech University) · 2019
Typedissertation
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveQuarter (Canadian coin)Tax creditTax incentivePoliticsTax reformValue-added taxIndirect tax
DOInot available

Abstract

fetched live from OpenAlex

One of the main factors that affects individual charitable giving behaviors is the availability of tax incentives. Thus, this study uses data from the Consumer Expenditure Survey (CEX) for the first quarter of 2004 through the fifth quarter of 2017 to examine the effects of tax incentives on contributions made to religious, educational, other, and political non-profit organizations and how these organizations are impacted by tax subsidies. While every type of contribution (except political contributions) is affected by the tax price of giving, other charitable donations are the most responsive to changes in the tax price of giving. The results also reveal that “income before taxes,” “race,” “gender,” “level of education,” “children in the household,” and “presence of persons over the age of 64” are important predictors of every type of giving. These findings can help academics and planners by increasing fundraising efforts and improving financial planning services.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThinkTech (Texas Tech University)Same topicLaser Design and ApplicationsFrench-language works237,207