Bibliographic record
Abstract
The contemporary model of economical production and consumption is not sustainable; if the pattern continues, humankind will need to scramble for resources. Currently, resource extraction from the earth is 1.7 times higher than her actual capacity (Watts, 2018). This paper stresses the importance of the shift from the current linear (take, make and dispose) modality to a circular (take, make and reutilize) model to maximize the value from a product by keeping it in the loop of circularity. In pursuit of this change in model, reverse logistics, performance economy, and sharing economy all need to be integrated in order to facilitate regenerative and restorative techniques which enable reusing, recycling, remanufacturing and refurbishing of resources. Businesses need to redesign and restructure their current processes so that they can reduce the consumption of resources, thus developing a competitive edge. Incineration and dumping of resources should be the last option. The assets that are able to sense, record and communicate information are referred to as “intelligent assets” which innovates “smart solutions” to enable a circular economy (MacArthur, 2016). But this paradigm shift is not possible alone through the effort of a single entity. Involvement and commitments from individual, regional, governmental and intra-governmental levels are mandatory as it helps to create a synergist effect.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".