Portal Impact Assessment: The NGO in Pakistan Case
Bibliographic record
Abstract
The purpose of this paper is to present the results of an end-of-project impact assessment (IA) of a Social Enterprise Development Center (SEDC) (created in Pakistan 7 years ago). The SEDC was created in 2000 to provide mainly training to non-government organizations (NGOs) and be a central recourse for their communication, resource sharing and support. The IA entailed the measurement of a large number of indicators spanning a wide range of stakeholder across various levels of impact chain: from individual to government. In this paper we present the results of the portal which was a central component of the project. A survey methodology approach was used and 280 members were asked to participate in a questionnaire online. Respondents to the survey indicated that they were somewhat satisfied with the achieved portal outcomes; in meeting their expectations; and that the components were favorable to their needs. However, they reported that they were not using the portal enough. Most participants agreed that the content is useful, clear, concise and accurate, but not (to a lesser extent perhaps) complete or current. Also, most agreed that the website interface is acceptable and readable with no complaints in terms of availabilities, loading speed, colors, organization, etc. Many expressed that the portal did not have adequate search facilities. Moreover, the data did not show any consensus among participants with regards to the portal helping them reach career objectives. The majority claimed that they access the portal mainly seeking to improve their skills, prepare for work related tasks and to get informed through latest news announcements.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".