Managing the lifecycle of IEEE's Humanitarian Technology with peer-review
Bibliographic record
Abstract
The objective of this paper is to describe a Product Lifecycle Management process for IEEE's Humanitarian Technology (HT) “Solutions” that leverages IEEE's peer-review. In 2011, the Humanitarian Initiatives Committee (HIC) of IEEE Region 7 organized a student design competition [1] with the specific objective of understanding the design process of HT “Solutions” to be made available as open source. The 2011 competition was an initial effort to understand open source licensing, open source hardware and the open source development model. Adoption of the open source development model was a stated objective of IEEE's Humanitarian technology Challenge (HTC) [2]. The HIC held a student paper competition in 2013. The goal of the 2013 competition was to nurture the growing interest in Humanitarian Technology in Region 7; to address HIC's concerns from the 2011 competition, more effort was focused in the paper review process for the 2013 competition and standard IEEE peer-review tools and processes were used. This document presents in Section III the lessons learned during these two competitions, and proposes in section IV a Product Lifecycle Management framework for the Intellectual Property Rights (IPR) of OSI development that leverages IEEE's peer-review process.
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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 | 0.056 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".