Sis Security White Paper: Managing Privacy And Security For The Service Information System
Bibliographic record
Abstract
The Service Information System (SIS) is a monitoring and evaluation platform built on open source software and donated Microsoft services that is offered on a subscription basis to nonprofits providing any kind of service. It is developed and managed by LogicalOutcomes, a Canadian nonprofit, and launched in March 2018. The first implementation was created in partnership with the Ontario Coalition of Agencies Serving Immigrants, funded by the Ontario Ministry of Citizenship and Immigration. Features of SIS include surveys and other data collection tools, a data warehouse, a metadata registry of validated indicators, training videos, a community data portal containing public datasets, and customized evaluation sites with Power BI reports for each subscribing organization. One of the main reasons we developed SIS was to protect the privacy of vulnerable people who are served by nonprofits. The responsible and secure use of personal data requires a great deal of ongoing effort and expertise, which most nonprofits are not equipped to provide. At the same time, nonprofits are expected by their funders to evaluate their services by collecting personal information from vulnerable clients. Our goal was to develop an effective monitoring and evaluation service that took care of most of the technical and administrative procedures, including security and privacy protections, so that nonprofits could focus on evaluation design and results. This paper describes the security policies, principles and procedures that have been built into SIS to protect the privacy of personal information, emphasizing our planned compliance with the EU General Data Protection Regulation (GDPR).
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 | 0.076 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".