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Record W7057827660

Massachusetts Working Cities Challenge Final Assessment of Round 1 Progress

2018· other· en· W7057827660 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2018
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityWork (physics)Post-industrial societyWorking groupGovernment (linguistics)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

In May 2013, the Federal Reserve Bank of Boston (Boston Fed) formally launched the Working Cities Challenge: An Initiative for Massachusetts Smaller Cities. The Working Cities Challenge (WCC) encourages and supports leaders from the business, government, philanthropy, and nonprofit sectors in smaller, postindustrial cities to work collaboratively on innovative strategies that have the potential to produce large-scale results for low-income residents in their communities. Ultimately, the Boston Fed expects that the teams' efforts will build the cities' civic infrastructure leading to long-term improved prosperity and opportunity for residents in Working Cities.The Boston Fed developed a competitive process for city selection in which a jury chose the winning cities with the grant award varying based on the strength of the cities' proposals. WCC announced in early 2014 the award of a total of $1.8 million in grants to six working cities. The competitive grants included four implementation grants ranging in size from $700,000 to $225,000 over a planned three-year period awarded to Chelsea, Fitchburg, Holyoke, and Lawrence. In addition, WCC awarded two smaller $100,000 one-year seed grants to Salem and Somerville. Based on the assessment of progress at the midpoint of the implementation period, the Boston Fed extended the grant cycle slightly and augmented the implementation grants. Following a second juried competitive application process, the Boston Fed awarded each of the four implementation cities an additional $150,000 and extended the grant period through September 2017, making implementation a full three-and-a-half years. Beyond the grant funds, the working cities have received technical assistance and opportunities for shared learning and peer exchange. While perhaps less tangible than technical assistance, but no less important, the working cities now have greater visibility and new forums for access to funders as well.Below is a presentation produced by Mt. Auburn Associates.Please find the full report, case studies, and additional resources here: https://www.bostonfed.org/workingcities/massachusetts/round1/process/evaluation.htm

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.640
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.6410.000

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 head0.031
GPT teacher head0.321
Teacher spread0.290 · 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
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
Published2018
Admission routes1
Has abstractyes

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