Bibliographic record
Abstract
From its beginning, the Seventh-day Adventist Church has measured progress.After all, if you aim to accomplish something but do not measure your progress toward that something, how will you know if you are actually making progress?Therefore, most of us agree we need to make some sort of measurements, which in this paper I refer to as metrics.However, what should we measure, and why?This article will focus on comprehensive metrics.To be sure, there are many more categories and types of metrics, but having a clear understanding of the purpose and place of comprehensive metrics will help us bring our mission into clearer focus. MandateA mandate typically outlines a task and any parameters associated with that task such as the scope of the task.The governing board of a company may decide that the scope of its marketing territory is just the 50 states of the United States and it communicates this to its executives.Those executives need not concern themselves with what is happening in Canada, Mexico, or Brazil.However, they certainly need to concern themselves with what is happening in the United States.That is their mandated scope and they definitely need one or more metrics to assess progress within that territory.For such a company, a comprehensive metric must measure the entire United States.Undoubtedly, they will want to have metrics that relate to only portions of their entire territory (certain states, top sales regions, cities of a certain size, etc.), but for sure they need to know what their comprehensive progress is within their entire mandated territory.The scope of the church's mandate is clearly the entire world and every living person in it (Acts 1:8; Mark 16:15).Therefore, while the church 1
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 | 0.022 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".