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PhiladelphiaOne number, traced

Keystone Literature proficiencyJULIA DE BURGOS ELEMENTARY, 2024-25

The share of students scoring proficient or advanced on the Keystone Literature exam. This figure is for students in special education.

Identifiers

school code 5170 · metric key keystone_literature_proficiency · year 2024-25 · subgroup IEP · population cut n/a

  1. 1
    What you seeKeystone Literature proficiency

    7.5%

    the share of students scoring proficient or advanced on the Keystone Literature exam.

    vs. peers
    45th percentile
    vs. citywide
    45th percentile
    unit
    percent
    direction
    higher is better
  2. 2
    How it was computedvalue = the state's published percentage for the school × subgroup, as-is
    1. Read the state's wide-format datafile: one row per Pennsylvania school, one column per measure × subgroup (e.g. PercentPersistentAttendance_Black).
    2. Keep Philadelphia rows by mapping the state's AUN + Schl codes to the district's ULCS code through the Master School List.
    3. Unpivot each recognized measure × subgroup column into one served cell; state suppression codes ('IS', '--', '*') are stored as suppressed.

    Citywide percentile, reconstructed right now

    Among 268 default-included schools with a value for this metric, year, and subgroup, this school has a higher value than 119 and ties 2. Midrank rule: (119 + 2/2) / (268 − 1) = 0.449.Stored: 0.449. ✓ matches

    How percentiles are computed · How peer groups are chosen

    Note: stored percentiles are computed by a batch script after loads; a difference from the live reconstruction means a newer load changed the comparison set and the batch has not been re-run.

    Evidence: metric tree: Keystone Literature proficiency

  3. 3
    The stored cellThe stored cell
    table
    philly_school_year_metrics
    value
    7.5
    denominator
    null (not published)
    suppressed
    false
    citywide_percentile
    0.4494
    comparison_group_percentile
    0.4500
    source_load_id
    8ba7d9e6-a94a-4a6f-9e9b-ceb2707d43de

    Evidence: data model overview

  4. 4
    The load that wrote itWritten on 2026-09-08 by futurereadypa_performance.ts
    source id
    futurereadypa_performance_2024-25
    SHA-256 of the file read
    a16ffc40e7d1851d5ac83dd63d8ca5348ead064e029bade593a0182ed085bc92
    bytes
    5,113,152
    year covered
    2024-25
    code commit
    c19ea6ec845c65bcc40eb49644be1c7e67b05f25

    To confirm you have the same file: download it from the URL above, then run shasum -a 256 <downloaded file> — the output should equal the hash above. If the publisher has since replaced the file, the hashes will differ, which is itself useful to know.

    Evidence: loader script · first-fetch findings for this file · source registry

  5. 5
    The publisher's fileFuture Ready PA Index datafile, 2024-25

    Pennsylvania Department of Education (Future Ready PA Index). The state's school-level accountability indicators for every public school in Pennsylvania — test proficiency, growth (PVAAS), persistent attendance, graduation — by subgroup.

    format
    XLSX (wide: one column per measure × subgroup)
    join key
    AUN + Schl (state IDs), mapped to ULCS via the Master School List
    • The chronic-absenteeism column exists in the file but is empty for individual Philadelphia schools; the state reports persistent attendance instead.
    • Publishes no student counts for the attendance measure.
    • Its 90%-attendance figure does not match the district's for the same schools (see the cross-publisher check).
  6. 6
    How it was checkedChecks: 0 passed, 1 not run
    • not runBase case against the state file not yet run for this metric (the loader is shared with keystone_algebra_proficiency, whose SDP-side base case passed)
  7. 7
    Assumptions & caveatsAssumptions and caveats
    • The state's AUN + Schl → ULCS mapping from the 2024-25 Master School List is correct. State rows for schools not in that directory — district schools that closed before 2024-25 — are not loaded, so every year of state data is a survivor-only panel; cyber charters have their own AUNs and are outside the district's directory.
    • Where two directory schools share a state code (a school and its Continuation Academy), the state's single row belongs to the main school (deterministic rule; the full absenteeism QA lists every shared code).
    • The state publishes no student counts for most measures, so served cells carry no denominator.
    • Served from both the district's and the state's files; check the cell's load record.

    Algorithms involved: Reading the state's wide-format datafile, Citywide and peer-group percentiles, Peer groups: 40 nearest neighbors on demographics

The same metric for this school, every year and cut

YearSubgroupCutValue
2021-22All studentsno population cut15.2%
2021-22American Indian / Alaska Native studentsno population cutwithheld
2021-22Asian studentsno population cutwithheld
2021-22Native Hawaiian / Pacific Islander studentsno population cutwithheld
2021-22Black studentsno population cut6.3%
2021-22Hispanic studentsno population cut17.0%
2021-22White studentsno population cutwithheld
2021-22Students of two or more racesno population cutwithheld
2021-22Economically disadvantaged studentsno population cut15.1%
2021-22English learnersno population cut11.7%
2021-22Students in special educationno population cut5.6%
2021-22Black, Hispanic, and multi-racial students combinedno population cut
2022-23All studentsno population cut14.3%
2022-23American Indian / Alaska Native studentsno population cutwithheld
2022-23Asian studentsno population cutwithheld
2022-23Native Hawaiian / Pacific Islander studentsno population cutwithheld
2022-23Black studentsno population cut7.0%
2022-23Hispanic studentsno population cut15.5%
2022-23White studentsno population cutwithheld
2022-23Students of two or more racesno population cutwithheld
2022-23Economically disadvantaged studentsno population cut14.4%
2022-23English learnersno population cut8.4%
2022-23Students in special educationno population cut7.0%
2022-23Black, Hispanic, and multi-racial students combinedno population cut13.7%
2023-24All studentsno population cut16.5%
2023-24American Indian / Alaska Native studentsno population cutwithheld
2023-24Asian studentsno population cutwithheld
2023-24Native Hawaiian / Pacific Islander studentsno population cutwithheld
2023-24Black studentsno population cut15.9%
2023-24Hispanic studentsno population cut16.1%
2023-24White studentsno population cutwithheld
2023-24Students of two or more racesno population cutwithheld
2023-24Economically disadvantaged studentsno population cut16.3%
2023-24English learnersno population cut10.1%
2023-24Students in special educationno population cut6.8%
2023-24Black, Hispanic, and multi-racial students combinedno population cut15.9%
2024-25All studentsno population cut18.0%
2024-25American Indian / Alaska Native studentsno population cutwithheld
2024-25Asian studentsno population cutwithheld
2024-25Native Hawaiian / Pacific Islander studentsno population cutwithheld
2024-25Black studentsno population cut16.4%
2024-25Hispanic studentsno population cut18.3%
2024-25White studentsno population cutwithheld
2024-25Students of two or more racesno population cutwithheld
2024-25Economically disadvantaged studentsno population cut17.3%
2024-25English learnersno population cut11.4%
2024-25Students in special educationno population cut7.5%
2024-25Black, Hispanic, and multi-racial students combinedno population cut18.0%