Framework for Assessing the Impact of Construction Research and Development on the Construction Industry and Academia
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.
Bibliographic record
Abstract
Academia and the construction industry are linked by a strong collaborative relationship through research and development (R&D); both have expectations for outcomes and impacts as incentives to maintain this relationship. However, assessing outcomes and measuring impacts is often challenging. To address this challenge, an evaluation framework for assessing the impact of construction R&D on the construction industry and academia is proposed in this paper. This framework consists of a “logic model” and an “evaluation plan” to define and evaluate construction R&D impacts on both the construction industry and academia. The logic model helps to define the relationship between academia and the construction industry in terms of inputs, outputs, and outcomes and impacts; this relationship is expressed using “if-then” rules to relate the inputs to outputs, and the outputs to outcomes and impacts. The evaluation plan helps determine the fulfillment degree of the expected outcomes and impacts from the perspectives of both the construction industry and academia. The proposed evaluation framework will help quantify and assess the impact of construction R&D on the construction industry and on academia so that the inputs of both parties can be better used to deliver the outcomes each expects.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it