A Scoping Review on Value Creation from Collaborations enabled by the Internet of Things in Agriculture
Bibliographic record
Abstract
The purpose of this review is to better understand the types of research relating to value creation from collaboration enabled by the Internet of Things in agriculture and clarify venues for future research. By applying the Joanna Briggs Institute Scoping Review Protocol, we identified 37 articles used for further analysis. The results indicate that the agricultural environment’s current structure is in the initial stages of collaboration enabled by IoT. Of the articles, the majority were published in the last three years, with publication steadily increasing per year. Publications were the most prevalent in India, China, the United States, Italy, and Canada. The majority of the studies did not possess a methodology, being categorised either as a conceptual study or industry report (14) or as not an official analysis (a news article in a magazine, newspaper, wire feed or trade journal) (12). The literature is multifaceted, and as a result, the papers were categorised into the themes of economic (20), legal (12), social (21), technical (25), and operational (18). Many authors did not connect value or collaboration to a measurable outcome. In the articles, value creation, value, and the Internet of Things was either not defined or had varying definitions. As a result of these collaborations, concerns include the ownership, privacy, and misuse of data. Future empirical research is required to define these concepts, concerns and create a framework of the Internet of Things discourse.
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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".