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Record W4391412898 · doi:10.3138/9781487535018-004

3 Discounting Now and Then

2019· book-chapter· en· W4391412898 on OpenAlexaboutno aff
Joseph Heath

Bibliographic record

VenueUniversity of Toronto Press eBooks · 2019
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsDiscountingEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Most major public policy initiatives have an impact on both social welfare and government finances, not just in the present, but extending out into the distant future.Since it is possible to distribute the benefits and burdens of these policies in different ways over time, such policies naturally raise questions of intergenerational justice.Unfortunately, the treatment of these questions has historically been somewhat ad hoc.This has been changing, driven in particular by the need to respond to the problem of anthropogenic climate change, an issue in which the distribution of burdens over time winds up being a significant determinant of policy choices.Perhaps the single most important factor determining this distribution is the social discount rate used in public sector cost-benefit analysis.The discount rate is used to calculate the present value of future costs and benefits, and as such literally determines how much we are obliged to care about the sorrows and triumphs of those yet unborn.The higher the discount rate, the lower the level of concern that must be shown, in the present, for future costs and benefits.Despite its evident importance, the social discount rate for a long time languished in obscurity.Historically, it was determined in a rather casual manner and the rates tended to be quite high.In Canada, for instance, the social discount rate was fixed in 1976 at 10 per cent. 1 This was considered plausible in most of the standard cases of application, which typically involved infrastructure projects.The only anomaly was nuclear waste disposal, which is necessarily concerned with events in the very distant future.Such an anomaly was relatively easy to ignore, however, because it is practically impossible to construct any sort of model in which it matters at all in the present what happens 10,000 years from now.However, as the use of cost-benefit analysis expanded and became standard in more policy domains -such as environmental regulation and healthcare resource allocation -the choice of discount 3 Discounting Now and Then joseph heath

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)0.005
metaresearch head score (Gemma)0.016
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: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0110.012
Open science0.0020.004
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0480.012

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 head0.024
GPT teacher head0.179
Teacher spread0.155 · 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
Published2019
Admission routes1
Has abstractyes

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