Framework for Assessing the Impact of Construction Research and Development on the Construction Industry and Academia
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".