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Record W2900532089 · doi:10.1177/0739456x211067271

Doctoral Education and the Academic Job Market in Planning

2022· article· en· W2900532089 on OpenAlexfundno aff
Joanna Ganning

Bibliographic record

VenueJournal of Planning Education and Research · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
FundersUniversity of Massachusetts AmherstUniversity of California, IrvineUniversity of California, Los AngelesUniversity of WaterlooUniversity of Colorado DenverUniversity of TorontoYork UniversityArizona State UniversityVirginia Commonwealth UniversityUniversity of South FloridaFlorida Atlantic UniversityGeorgia State UniversityUniversity of CincinnatiClemson UniversityUniversity of LouisvilleUniversity of OklahomaCollege of Engineering, Michigan State UniversityUniversity of WashingtonUniversity of Illinois at Urbana-ChampaignMichigan State UniversityAuburn UniversityUniversidad del AtlánticoUniversity of MinnesotaUniversity of PennsylvaniaUniversity of AlbertaMassachusetts Institute of TechnologyCleveland State UniversityPortland State UniversityHarvard UniversityJackson State UniversityUniversity of Illinois at ChicagoConnaught FundFlorida State UniversityOhio State UniversityUniversity College LondonUniversity of Southern California
KeywordsJob marketBusinessPublic relationsWork (physics)Medical educationPedagogySociologyPolitical scienceLabour economicsEconomicsEngineeringMedicine

Abstract

fetched live from OpenAlex

This project uses three years (2017–2020) of survey data and job announcements to analyze the alignment between doctoral education and the academic job market in Planning. Graduates are competitive, having teaching experience and published or publishable research. The primary job market (i.e., the Association of Collegiate Schools of Planning [ACSP] Career Center) likely accommodates 50 to 60 percent of graduates finding academic employment (or about 25% of graduating cohorts), with a large share navigating the secondary job market. Survey data from program directors suggest approximately one-third of graduates do not aspire to academic careers. This paper illustrates realities of academic employment for recent graduates and includes recommendations for programs.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.182
GPT teacher head0.429
Teacher spread0.246 · 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.

Study designObservational
DomainIncentives
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

Citations4
Published2022
Admission routes1
Has abstractyes

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