Promotion Strategy of Low-Carbon Consumption of Fresh Food Based on Willingness Behavior
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The research on the influencing factors of residents’ low-carbon consumption willingness and low-carbon consumption behavior of fresh food has certain practical guiding significance. Existing studies have analyzed the low-carbon consumption willingness, but the factors considered are not comprehensive and the degree of fit needs to be improved. Therefore, this paper starts with 37 variables from six aspects: demographic factors, psychological factors, low-carbon related knowledge, external factors, policy norms, and product factors. The binary logistic model is used to carry out regression analysis on low-carbon consumption willingness and low-carbon consumption behavior, and the fitting degree is higher and reaches about 90%. The regression results show that sense of responsibility, government tax, low-carbon product quality, and low-carbon product price have a significant impact on residents’ low-carbon consumption willingness. Whether there are fake and shoddy products in the market and whether the products are really of low carbon have a significant impact on low-carbon consumption behavior. Finally, starting from the three subjects of government, enterprises, and residents, this paper puts forward targeted suggestions to improve residents’ low-carbon consumption willingness and promote residents’ low-carbon consumption behavior, in order to promote low-carbon consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".