MétaCan
Menu
Back to cohort
Record W1799084732 · doi:10.3386/w16877

The Effect of Evaluation on Performance: Evidence from Longitudinal Student Achievement Data of Mid-career Teachers

2011· report· en· W1799084732 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNational Bureau of Economic Research · 2011
Typereport
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsManning Diversified Forest Products (Canada)
FundersInstitute of Education SciencesHarvard UniversityJoyce FoundationU.S. Department of Education
KeywordsMathematics educationLongitudinal dataStudent achievementPsychologyAcademic achievementEconometricsMedical educationComputer scienceMathematicsMedicineData mining

Abstract

fetched live from OpenAlex

The effect of evaluation on employee performance is traditionally studied in the context of the principal-agent problem. Evaluation can, however, also be characterized as an investment in the evaluated employee's human capital. We study a sample of mid-career public school teachers where we can consider these two types of evaluation effect separately. Employee evaluation is a particularly salient topic in public schools where teacher effectiveness varies substantially and where teacher evaluation itself is increasingly a focus of public policy proposals. We find evidence that a quality classroom-observation-based evaluation and performance measures can improve mid-career teacher performance both during the period of evaluation, consistent with the traditional predictions; and in subsequent years, consistent with human capital investment. However the estimated improvements during evaluation are less precise. Additionally, the effects sizes represent a substantial gain in welfare given the program's costs.

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.

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)0.050
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.675
GPT teacher head0.603
Teacher spread0.072 · 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